Gregory Dudek

ICRA Conference 2025 Conference Paper

Generalizable Imitation Learning Through Pre-Trained Representations

• Wei-Di Chang
• Francois Robert Hogan
• Scott Fujimoto
• David Meger
• Gregory Dudek

In this paper, we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abilities of imitation learning policies. We introduce DVK, an imitation learning algorithm that leverages rich pre-trained Visual Transformer patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into groups associated with semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We demonstrate how this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. To facilitate further study of generalization in Imitation Learning, all of our code for the method and evaluation, as well as the dataset, is made available.

ICRA Conference 2025 Conference Paper

Learning Active Tactile Perception Through Belief-Space Control

• Jean-François Tremblay
• David Meger
• Francois Robert Hogan
• Gregory Dudek

Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects. We propose a method that autonomously learns tactile exploration policies by developing a generative world model that is leveraged to 1) estimate the object's physical parameters using a differentiable Bayesian filtering algorithm and 2) develop an exploration policy using an information-gathering model predictive controller. We evaluate our method on three simulated tasks where the goal is to estimate a desired object property (mass, height or toppling height) through physical interaction. We find that our method is able to discover policies that efficiently gather information about the desired property in an intuitive manner. Finally, we validate our method on a real robot system for the height estimation task, where our method is able to successfully learn and execute an information-gathering policy from scratch.

ICRA Conference 2024 Conference Paper

A Neural-Evolutionary Algorithm for Autonomous Transit Network Design

• Andrew Holliday
• Gregory Dudek

Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses. We propose a novel algorithm for planning networks of routes for autonomous buses. We first train a graph neural net model as a policy for constructing route networks, and then use the policy as one of several mutation operators in a evolutionary algorithm. We evaluate this algorithm on a standard set of benchmarks for transit network design, and find that it outperforms the learned policy alone by up to 20% and a plain evolutionary algorithm approach by up to 53% on realistic benchmark instances.

ICRA Conference 2024 Conference Paper

CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots

• Dmitriy Rivkin
• Nikhil Kakodkar
• Francois Robert Hogan
• Bobak H. Baghi
• Gregory Dudek

This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation. We focus on following complex instructions that are more akin to natural conversation than traditional explicit procedural directives typically seen in robotics. Unlike most prior work where navigation directives are provided as simple imperative commands (e. g. , "go to the fridge"), we examine implicit directives obtained through conversational interactions. We leverage the 3D simulator AI2Thor to create household query scenarios at scale, and augment it by adding complex language queries for 40 object types. We demonstrate that a robot using our method CARTIER (Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots) can parse descriptive language queries up to 42% more reliably than existing LLM-enabled methods by exploiting the ability of LLMs to interpret the user interaction in the context of the objects in the scenario.

RLC Conference 2024 Conference Paper

Imitation Learning from Observation through Optimal Transport

• Wei-Di Chang
• Scott Fujimoto
• David Meger
• Gregory Dudek

Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance of demonstrated actions. In this paper, we re-examine optimal transport for IL, in which a reward is generated based on the Wasserstein distance between the state trajectories of the learner and expert. We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning. Unlike many other state-of-the-art methods, our approach can be integrated with any RL algorithm and is amenable to ILfO. We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting, achieving expert-level performance across a range of evaluation domains even when observing only a single expert trajectory without actions.

RLJ Journal 2024 Journal Article

Imitation Learning from Observation through Optimal Transport

• Wei-Di Chang
• Scott Fujimoto
• David Meger
• Gregory Dudek

Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance of demonstrated actions. In this paper, we re-examine optimal transport for IL, in which a reward is generated based on the Wasserstein distance between the state trajectories of the learner and expert. We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning. Unlike many other state-of-the-art methods, our approach can be integrated with any RL algorithm and is amenable to ILfO. We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting, achieving expert-level performance across a range of evaluation domains even when observing only a single expert trajectory without actions.

IROS Conference 2024 Conference Paper

PhotoBot: Reference-Guided Interactive Photography via Natural Language

• Oliver Limoyo
• Jimmy Li 0001
• Dmitriy Rivkin
• Jonathan Kelly
• Gregory Dudek

We introduce PhotoBot, a framework for fully automated photo acquisition based on an interplay between high-level human language guidance and a robot photographer. We propose to communicate photography suggestions to the user via reference images that are selected from a curated gallery. We leverage a visual language model (VLM) and an object detector to characterize the reference images via textual descriptions and then use a large language model (LLM) to retrieve relevant reference images based on a user’s language query through text-based reasoning. To correspond the reference image and the observed scene, we exploit pretrained features from a vision transformer capable of capturing semantic similarity across marked appearance variations. Using these features, we compute suggested pose adjustments for an RGB-D camera by solving a perspective-n-point (PnP) problem. We demonstrate our approach using a manipulator equipped with a wrist camera. Our user studies show that photos taken by PhotoBot are often more aesthetically pleasing than those taken by users themselves, as measured by human feedback. We also show that PhotoBot can generalize to other reference sources such as paintings.

ICRA Conference 2024 Conference Paper

Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model

• Faraz Lotfi
• Khalil Virji
• Farnoosh Faraji
• Lucas Berry
• Andrew Holliday
• David Meger
• Gregory Dudek

In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning (RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance of our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle’s model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle’s kinematic model for enhanced decision-making. The code and the evaluation data are available at (Github-repo).

IROS Conference 2024 Conference Paper

Working Backwards: Learning to Place by Picking

• Oliver Limoyo
• Abhisek Konar
• Trevor Ablett
• Jonathan Kelly
• Francois Robert Hogan
• Gregory Dudek

We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific, contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention using two modules: compliant control for grasping and tactile regrasping. We train a policy directly from visual observations through behavioural cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e. g. , placing a plate picked up from a table). We validate our approach in home robot scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of success rate and data efficiency, while requiring no human supervision.

IROS Conference 2023 Conference Paper

A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms

• Hanqing Zhao
• Alexandre Pacheco
• Volker Strobel
• Andreagiovanni Reina
• Xue Liu 0001
• Gregory Dudek
• Marco Dorigo

Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robotics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.

ICRA Conference 2023 Conference Paper

ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

• Dmitriy Rivkin
• Gregory Dudek
• Nikhil Kakodkar
• David Meger
• Oliver Limoyo
• Michael Jenkin
• Xue Liu 0004
• Francois Robert Hogan

Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.

AAAI Conference 2023 Conference Paper

Hypernetworks for Zero-Shot Transfer in Reinforcement Learning

• Sahand Rezaei-Shoshtari
• Charlotte Morissette
• Francois R. Hogan
• Gregory Dudek
• David Meger

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.

IROS Conference 2023 Conference Paper

Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning

• Hanqing Zhao
• Xue Liu 0001
• Gregory Dudek

We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i. e. fault detection). Current IRL methods for fault detection, however, either use a linear reward representation or require extensive sampling from the environment to estimate the policy, rendering them inappropriate for safety-critical situations where sampling of failure observations via fault injection can be expensive and dangerous. To address this issue, this paper proposes a zero-shot and exogenous fault detector based on an approximate variational reward imitation learning (AVRIL) structure. The fault detector recovers a reward signal as a function of externally observable information to describe the normal operation, which can then be used to detect anomalies caused by faults. Our method incorporates expert knowledge through a customizable reward prior distribution, allowing the fault detector to learn the reward solely from normal operation samples, without the need for a simulator or costly interactions with the environment. We evaluate our approach for exogenous partial fault detection in multi-stage robotic manipulator tasks, comparing it with several baseline methods. The results demonstrate that our method more effectively identifies unseen faults even when they occur within just three controller time steps.

IROS Conference 2022 Conference Paper

Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model

• Hanqing Zhao
• Travis Manderson
• Hao Zhang
• Xue Liu 0004
• Gregory Dudek

We propose an approach that enables simultaneous interpretable learning of a high-level discrete behaviour and its low-level rhythmic sub-behaviour. We do this though a unified reward function, where a reward function that only describes low-level behaviour, with less impact on learning of other behaviours is recovered from few-shot motion demonstrations. To this end, we first extract local behaviour motifs from state-only human demonstrations and random driving samples using an adaptive motif discovery approach derived from the Matrix Profile algorithm. We then optimize parameters for motif discovery by maximizing the sum and entropy over motif sizes. Interacting Multiple Model (IMM) estimators are constructed on top of linear-Gaussian dynamics of discovered motifs, the cumulative distributions over motifs estimated by IMMs serve as the basis of the reward function. By combining the recovered reward with the terrain type signal gathered from the environment, we are able to train a dual-objective off-road vehicle controller that demonstrates both terrain selection and human-like driving behaviours. Compared with related approaches across 10 people, our rhythmic behaviour reward recovery approach enables the controller to produce higher preference over human driving demonstrations. In addition to performing more stable across different people with 87% less variance than the best baseline in rhythmic behaviour indicator, our method reduces the negative effects on higher-level behaviour learning while maintaining high interpretability at all stages of the algorithm.

IROS Conference 2022 Conference Paper

SESNO: Sample Efficient Social Navigation from Observation

• Bobak H. Baghi
• Abhisek Konar
• Francois Robert Hogan
• Michael Jenkin
• Gregory Dudek

In this paper, we present the Sample Efficient Social Navigation from Observation (SESNO) algorithm that efficiently learns socially-compliant navigation policies from observations of human trajectories. SESNO is an inverse reinforcement learning (IRL)-based algorithm that learns from human trajectory observations without knowledge of their actions. We improve the sample-efficiency over previous IRL-based methods by introducing a shared experience replay buffer that allows reuse of past trajectory experiences to estimate the policy and the reward. We evaluate SESNO using publicly available pedestrian motion data sets and compare its performance to related baseline methods in the literature. We show that SESNO yields performance superior to existing baselines while dramatically improving the sample complexity by using as few as a hundredth of the samples required by existing baselines.

ICRA Conference 2022 Conference Paper

Visuotactile-RL: Learning Multimodal Manipulation Policies with Deep Reinforcement Learning

• Johanna Hansen
• Francois Robert Hogan
• Dmitriy Rivkin
• David Meger
• Michael Jenkin
• Gregory Dudek

Manipulating objects with dexterity requires timely feedback that simultaneously leverages the senses of vision and touch. In this paper, we focus on the problem setting where both visual and tactile sensors provide pixel-level feedback for Visuotactile reinforcement learning agents. We investigate the challenges associated with multimodal learning and propose several improvements to existing RL methods; including tactile gating, tactile data augmentation, and visual degradation. When compared with visual-only and tactile-only baselines, our Visuotactile-RL agents showcase (1) significant improvements in contact-rich tasks; (2) improved robustness to visual changes (lighting/camera view) in the workspace; and (3) resilience to physical changes in the task environment (weight/friction of objects).

IROS Conference 2021 Conference Paper

Latent Attention Augmentation for Robust Autonomous Driving Policies

• Ran Cheng
• Christopher Agia
• Florian Shkurti
• David Meger
• Gregory Dudek

Model-free reinforcement learning has become a viable approach for vision-based robot control. However, sample complexity and adaptability to domain shifts remain persistent challenges when operating in high-dimensional observation spaces (images, LiDAR), such as those that are involved in autonomous driving. In this paper, we propose a flexible framework by which a policy’s observations are augmented with robust attention representations in the latent space to guide the agent’s attention during training. Our method encodes local and global descriptors of the augmented state representations into a compact latent vector, and scene dynamics are approximated by a recurrent network that processes the latent vectors in sequence. We outline two approaches for constructing attention maps; a supervised pipeline leveraging semantic segmentation networks, and an unsupervised pipeline relying only on classical image processing techniques. We conduct our experiments in simulation and test the learned policy against varying seasonal effects and weather conditions. Our design decisions are supported in a series of ablation studies. The results demonstrate that our state augmentation method both improves learning efficiency and encourages robust domain adaptation when compared to common end-to-end frameworks and methods that learn directly from intermediate representations.

AAAI Conference 2021 Conference Paper

Learning Intuitive Physics with Multimodal Generative Models

• Sahand Rezaei-Shoshtari
• Francois R. Hogan
• Michael Jenkin
• David Meger
• Gregory Dudek

Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions. This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. Visual information captures object properties such as 3D shape and location, while tactile information provides critical cues about interaction forces and resulting object motion when it makes contact with the environment. Utilizing a novel See-Through-your-Skin (STS) sensor that provides high resolution multimodal sensing of contact surfaces, our system captures both the visual appearance and the tactile properties of objects. We interpret the dual stream signals from the sensor using a Multimodal Variational Autoencoder (MVAE), allowing us to capture both modalities of contacting objects and to develop a mapping from visual to tactile interaction and vice-versa. Additionally, the perceptual system can be used to infer the outcome of future physical interactions, which we validate through simulated and realworld experiments in which the resting state of an object is predicted from given initial conditions.

ICRA Conference 2021 Conference Paper

Multimodal dynamics modeling for off-road autonomous vehicles

• Jean-François Tremblay
• Travis Manderson
• Aurélio Noca
• Gregory Dudek
• David Meger

Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot’s environment is thus crucial when building a model to perform predictions about the robot’s dynamics with the goal of doing motion planning. We design a model capable of long-horizon motion predictions, leveraging vision, lidar and proprioception, which is robust to arbitrarily missing modalities at test time. We demonstrate in simulation that our model is able to leverage vision to predict traction changes. We then test our model using a real-world challenging dataset of a robot navigating through a forest, performing predictions in trajectories unseen during training. We try different modality combinations at test time and show that, while our model performs best when all modalities are present, it is still able to perform better than the baseline even when receiving only raw vision input and no proprioception, as well as when only receiving proprioception. Overall, our study demonstrates the importance of leveraging multiple sensors when doing dynamics modeling in outdoor conditions.

ICRA Conference 2021 Conference Paper

Optimizing Cellular Networks via Continuously Moving Base Stations on Road Networks

• Yogesh A. Girdhar
• Dmitriy Rivkin
• Di Wu 0044
• Michael Jenkin
• Xue Liu 0004
• Gregory Dudek

Although existing cellular network base stations are typically immobile, the recent development of small form factor base stations and self driving cars has enabled the possibility of deploying a team of continuously moving base stations that can reorganize the network infrastructure to adapt to changing network traffic usage patterns. Given such a system of mobile base stations (MBSes) that can freely move on the road, how should their path be planned in an effort to optimize the experience of the users? This paper addresses this question by modeling the problem as a Markov Decision Process where the actions correspond to the MBSes deciding which direction to go at traffic intersections; states corresponds to the position of MBSes; and rewards correspond to minimization of packet loss in the network. A Monte Carlo Tree Search (MCTS)-based anytime algorithm that produces path plans for multiple base stations while optimizing expected packet loss is proposed. Simulated experiments in the city of Verdun, QC, Canada with varying user equipment (UE) densities and random initial conditions show that the proposed approach consistently outperforms myopic planners, and is able to achieve near-optimal performance.

IROS Conference 2021 Conference Paper

Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain

• Stefan Wapnick
• Travis Manderson
• David Meger
• Gregory Dudek

We present a reward-predictive, model-based learning method featuring trajectory-constrained visual attention for use in mapless, local visual navigation tasks. Our method learns to place visual attention at locations in latent image space which follow trajectories caused by vehicle control actions to later enhance predictive accuracy during planning. Our attention model is jointly optimized by the task-specific loss and additional trajectory-constraint loss, allowing adaptability yet encouraging a regularized structure for improved generalization and reliability. Importantly, visual attention is applied in latent feature map space instead of raw image space to promote efficient planning. We validated our model in visual navigation tasks of planning low turbulence, collision-free trajectories in off-road settings and hill climbing with locking differentials in the presence of slippery terrain. Experiments involved randomized procedural generated simulation and real-world environments. We found our method improved generalization and learning efficiency when compared to no-attention and self-attention alternatives.

IROS Conference 2020 Conference Paper

DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

• Bharat Joshi
• Md. Modasshir
• Travis Manderson
• Hunter Damron
• Marios Xanthidis
• Alberto Quattrini Li
• Ioannis M. Rekleitis
• Gregory Dudek

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves in a communication-constrained underwater environment is essential for many applications such as underwater exploration, mapping, multi-robot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image-to-image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP. Experimental results in real-world underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness and accuracy of the proposed technique in terms of translation error and orientation error over the state-of-the-art methods. The code is publicly available.

IROS Conference 2020 Conference Paper

Learning Domain Randomization Distributions for Training Robust Locomotion Policies

• Melissa Mozifian
• Juan Camilo Gamboa Higuera
• David Meger
• Gregory Dudek

This paper considers the problem of learning behaviors in simulation without knowledge of the precise dynamical properties of the target robot platform(s). In this context, our learning goal is to mutually maximize task efficacy on each environment considered and generalization across the widest possible range of environmental conditions. The physical parameters of the simulator are modified by a component of our technique that learns the Domain Randomization (DR) that is appropriate at each learning epoch to maximally challenge the current behavior policy, without being overly challenging, which can hinder learning progress. This so-called sweet spot distribution is a selection of simulated domains with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution; and 2) The DR distribution made as wide as possible, to increase variability in the environments. These properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the DR distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment 1.

ICRA Conference 2020 Conference Paper

Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images

• Travis Manderson
• Stefan Wapnick
• David Meger
• Gregory Dudek

We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual inputs. Our approach applies a hybrid model-based and model-free reinforcement learning method that is entirely self-supervised in labeling terrain roughness and collisions using on-board sensors. Notably, we provide both first-person and overhead aerial image inputs to our model. We nd that the fusion of these complementary inputs improves planning foresight and makes the model robust to visual obstructions. Our results show the ability to generalize to environments with plentiful vegetation, various types of rock, and sandy trails. During evaluation, our policy attained 90% smooth terrain traversal and reduced the proportion of rough terrain driven over by 6. 1 times compared to a model using only first-person imagery. Video and project details can be found at www. cim. mcgill. ca/mrl/offroad_driving/.

IROS Conference 2020 Conference Paper

One-Shot Informed Robotic Visual Search in the Wild

• Karim Koreitem
• Florian Shkurti
• Travis Manderson
• Wei-Di Chang
• Juan Camilo Gamboa Higuera
• Gregory Dudek

We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that currently perform monitoring tasks in unstructured natural environments navigate via path-tracking a pre-specified sequence of waypoints. Although this navigation method is often necessary, it is limiting because the robot does not have a model of what the scientist deems to be relevant visual observations. Thus, the robot can neither visually search for particular types of objects, nor focus its attention on parts of the scene that might be more relevant than the pre-specified waypoints and viewpoints. In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection. We propose and evaluate a weakly supervised video representation learning method that outperforms ImageNet embeddings for similarity tasks in the underwater domain. We also demonstrate the deployment of this similarity operator during informed visual navigation in collaborative environmental monitoring scenarios, in large-scale field trials, where the robot and a human scientist collaboratively search for relevant visual content. Code: https://github.com/rvl-lab-utoronto/visual_search_in_the_wild.

IROS Conference 2020 Conference Paper

PresSense: Passive Respiration Sensing via Ambient WiFi Signals in Noisy Environments

• Yi Tian Xu
• Xi Chen 0009
• Xue Liu 0004
• David Meger
• Gregory Dudek

Passive sensing with ambient WiFi signals is a promising technique that will enable new types of human-robot interactions while preserving users' privacy. Here, we present PresSense, a system for human respiration sensing in noisy environments. Unlike existing WiFi-based respiration sensors, we employ a human presence detector, improving the robustness in scenarios where no human is present in an Area Of Interest (AOI). We also integrate our novel feature, Peak Distance Histogram (PDH), with other classic WiFi features to achieve better accuracy when someone is present in the AOI. We tested our system using commodity WiFi devices in an office room. Our PresSense outperforms the state of the arts in both respiration rate estimation and presence detection.

ICRA Conference 2020 Conference Paper

View-Invariant Loop Closure with Oriented Semantic Landmarks

• Jimmy Li 0001
• Karim Koreitem
• David Meger
• Gregory Dudek

Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving localization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.

ICRA Conference 2019 Conference Paper

Generating Adversarial Driving Scenarios in High-Fidelity Simulators

• Yasasa Abeysirigoonawardena
• Florian Shkurti
• Gregory Dudek

In recent years self-driving vehicles have become more commonplace on public roads, with the promise of bringing safety and efficiency to modern transportation systems. Increasing the reliability of these vehicles on the road requires an extensive suite of software tests, ideally performed on high-fidelity simulators, where multiple vehicles and pedestrians interact with the self-driving vehicle. It is therefore of critical importance to ensure that self-driving software is assessed against a wide range of challenging simulated driving scenarios. The state of the art in driving scenario generation, as adopted by some of the front-runners of the self-driving car industry, still relies on human input [1]. In this paper we propose to automate the process using Bayesian optimization to generate adversarial self-driving scenarios that expose poorly-engineered or poorly-trained self-driving policies, and increase the risk of collision with simulated pedestrians and vehicles. We show that by incorporating the generated scenarios into the training set of the self-driving policy, and by fine-tuning the policy using vision-based imitation learning we obtain safer self-driving behavior.

ICRA Conference 2019 Conference Paper

Semantic Mapping for View-Invariant Relocalization

• Jimmy Li 0001
• David Meger
• Gregory Dudek

We propose a system for visual simultaneous localization and mapping (SLAM) that combines traditional local appearance-based features with semantically meaningful object landmarks to achieve both accurate local tracking and highly view-invariant object-driven relocalization. Our mapping process uses a sampling-based approach to efficiently infer the 3D pose of object landmarks from 2D bounding box object detections. These 3D landmarks then serve as a view-invariant representation which we leverage to achieve camera relocalization even when the viewing angle changes by more than 125 degrees. This level of view-invariance cannot be attained by local appearance-based features (e. g. SIFT) since the same set of surfaces are not even visible when the viewpoint changes significantly. Our experiments show that even when existing methods fail completely for viewpoint changes of more than 70 degrees, our method continues to achieve a relocalization rate of around 90%, with a mean rotational error of around 8 degrees.

ICRA Conference 2019 Conference Paper

Underwater Communication Using Full-Body Gestures and Optimal Variable-Length Prefix Codes

• Karim Koreitem
• Jimmy Li 0001
• Ian Karp
• Travis Manderson
• Gregory Dudek

In this paper we consider inter-robot communication in the context of joint activities. In particular, we focus on convoying and passive communication for radio-denied environments by using whole-body gestures to provide cues regarding future actions. We develop a communication protocol whereby information described by codewords is transmitted by a series of actions executed by a swimming robot. These action sequences are chosen to optimize robustness and transmission duration given the observability, natural activity of the robot and the frequency of different messages. Our approach uses a convolutional network to make core observations of the pose of the robot being tracked, which is sending messages. The observer robot then uses an adaptation of classical decoding methods to infer a message that is being transmitted. The system is trained and validated using simulated data, tested in the pool and is targeted for deployment in the open ocean. Our decoder achieves. 94 precision and. 66 recall on real footage of robot gesture execution recorded in a swimming pool.

IROS Conference 2018 Conference Paper

Coverage Optimization with Non-Actuated, Floating Mobile Sensors using Iterative Trajectory Planning in Marine Flow Fields

• Johanna Hansen
• Gregory Dudek

This paper considers a spatial coverage problem in which a network of passive floating sensors is used to collect samples in a body of water. We employ an iterative measurement and modeling scheme to incrementally deploy sensors so as to achieve spatial coverage, despite only controlling the initial sample point. Once deployed, sensors are moved about a survey area by ambient surface currents. We demonstrate our results in simulation on 40 different ocean flow fields and compare against several baselines. This work provides a computational tool for scientists seeking a low-cost, autonomous marine surveying system. Although in this paper, we concentrate on ocean drifters, our approach can be extended to other domains where a spatial distribution of passive nodes in a flow field can be modeled.

ICRA Conference 2018 Conference Paper

Heterogeneous Multi-Robot System for Exploration and Strategic Water Sampling

• Sandeep Manjanna
• Alberto Quattrini Li
• Ryan N. Smith
• Ioannis M. Rekleitis
• Gregory Dudek

Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. In this paper, the focus is on algorithms for efficient measurement and sampling using a multi-robot, data-driven, water-sampling behavior, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles (ASVs), one equipped with a water quality sensor and the other equipped with a water-sampling apparatus. The ASV with the sensor acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The ASV equipped with the water sampling apparatus makes decisions in real time on where to sample the water based on the suggestions made by the explorer robot. We evaluate the system in the context of measuring chlorophyll distributions. We do this both in simulation based on real geophysical data from MODIS measurements, and on real robots in a water reservoir. We demonstrate the effectiveness of the proposed approach in several ways including in terms of mean error in the interpolated data as a function of distance traveled.

ICRA Conference 2018 Conference Paper

Model-Based Probabilistic Pursuit via Inverse Reinforcement Learning

• Florian Shkurti
• Nikhil Kakodkar
• Gregory Dudek

We address the integrated prediction, planning, and control problem that enables a single follower robot (the photographer) to quickly re-establish visual contact with a moving target (the subject) that has escaped the follower's field of view. We deal with this scenario, which reactive controllers are typically ill-equipped to handle, by making plausible predictions about the long- and short-term behavior of the target, and planning pursuit paths that will maximize the chance of seeing the target again. At the core of our pursuit method is the use of predictive models of target behavior, which help narrow down the set of possible future locations of the target to a few discrete hypotheses, as well as the use of combinatorial search in physical space to check those hypotheses efficiently. We model target behavior in terms of a learned navigation reward function, using Inverse Reinforcement Learning, based on semantic terrain features of satellite maps. Our pursuit algorithm continuously predicts the latent destination of the target and its position in the future, and relies on efficient graph representation and search methods in order to navigate to locations at which the target is most likely to be seen at an anticipated time. We perform extensive evaluation of our predictive pursuit algorithm over multiple satellite maps, thousands of simulation scenarios, against state-of-the art MDP and POMDP solvers. We show that our method significantly outperforms them by exploiting domain-specific knowledge, while being able to run in real-time.

IROS Conference 2018 Conference Paper

Scale-Robust Localization Using General Object Landmarks

• Andrew Holliday
• Gregory Dudek

Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances. Existing approaches, however, are robust only up to about a 3× difference in scale between map and query images. We propose a novel combination of deep-learning-based object features and state-of-the-art SIFT point-features that yields improved robustness to scale change. This technique is training-free and class-agnostic, and in principle can be deployed in any environment out-of-the-box. We evaluate the proposed technique on the KITTI Odometry benchmark and on a novel dataset of outdoor images exhibiting changes in visual scale of 7× and greater, which we have released to the public. Our technique consistently outperforms localization using either SIFT features or the proposed object features alone, achieving both greater accuracy and much lower failure rates under large changes in scale.

IROS Conference 2018 Conference Paper

Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning

• Juan Camilo Gamboa Higuera
• David Meger
• Gregory Dudek

We present an algorithm for rapidly learning neural network policies for robotics systems. The algorithm follows the model-based reinforcement learning paradigm and improves upon existing algorithms: PILeO and a sample-based version of PILeo with neural network dynamics (Deep-PILeO). To improve convergence, we propose a model-based algorithm that uses fixed random numbers and clips gradients during optimization. We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. These improvements enable data-efficient synthesis of complex neural network policies. We test our approach on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with that of PILeO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex tasks.

IROS Conference 2018 Conference Paper

Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection

• Travis Manderson
• Juan Camilo Gamboa Higuera
• Ran Cheng
• Gregory Dudek

We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs.

ICRA Conference 2017 Conference Paper

Adapting learned robotics behaviours through policy adjustment

• Juan Camilo Gamboa Higuera
• David Meger
• Gregory Dudek

We present an approach to learning control policies for physical robots that achieves high efficiency by adjusting existing policies that have been learned on similar source systems, such as a similar robot with different physical parameters, or an approximate dynamics model simulator. This can be viewed as calibrating a policy learned on a source system, to match a desired behaviour in similar target systems. Our approach assumes that the trajectories described by the source robot are feasible on the target robot. By making this assumption, we only need to learn a mapping from the source robot state and action spaces to the target robot action space, which we call a policy adjustment model. We demonstrate our approach in simulation in the cart-pole balancing task and a two link double pendulum. We also validate our approach with a physical cart-pole system, where we adjust a learned policy under changes to the weight of the pole.

IROS Conference 2017 Conference Paper

Context-coherent scenes of objects for camera pose estimation

• Jimmy Li 0001
• David Meger
• Gregory Dudek

We propose an approach to vision-based pose estimation using object recognition and identity. Whereas feature based scene recognition and pose estimation methods are well established as effective means for estimating motion and recognizing locations, feature-based methods depend critically on the detection of common local features from one view of a scene to another. We focus on place recognition and pose change estimation in the context of large changes in viewing position, even to the extent that no common surfaces are seen between the two views. Our approach is based on using object identities and their inter-relationship to compute pose change. An important secondary outcome of our method is that it simultaneously infers the 3D poses of objects in the scene that are used as features. Such an object-based approach is inspired by a vast literature on human perception and has the potential for great robustness, albeit at the expense of accuracy. We propose a formulation of the problem using pairwise contextual constraints and develop an efficient algorithmic solution. We validate the approach and quantify its performance using the publicly available TUM SLAM dataset [1].

IROS Conference 2017 Conference Paper

Data-driven selective sampling for marine vehicles using multi-scale paths

• Sandeep Manjanna
• Gregory Dudek

This paper addresses adaptive coverage of a spatial field without prior knowledge. Our application in this paper is to cover a region of the sea surface using a robotic boat, although the algorithmic approach has wider applicability. We propose an anytime planning technique for efficient data gathering using point-sampling based on non-uniform data-driven coverage. Our goal is to sense a particular region of interest in the environment and be able to reconstruct the measured spatial field. Since there are autonomous agents involved, there is a need to consider the costs involved in terms of energy consumed and time required to finish the task. An ideal map of the scalar field requires complete coverage of the region, but can be approximated by a good sparse coverage strategy along with an efficient interpolation technique. We propose to optimize the trade off between the environmental field mapping and the costs (energy consumed, time spent, and distance traveled) associated with sensing. We present an anytime algorithm for sampling the environment adaptively by following a multi-scale path to produce a variable resolution map of the spatial field. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a spatial field spending minimum energy. We present results that indicate our sampling technique gathering most informative samples with least travel. We validate our approach through simulations and test the system on real robots in the open ocean.

ICRA Conference 2017 Conference Paper

Phytoplankton hotspot prediction with an unsupervised spatial community model

• Arnold Kalmbach
• Yogesh A. Girdhar
• Heidi M. Sosik
• Gregory Dudek

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.

IROS Conference 2017 Conference Paper

Topologically distinct trajectory predictions for probabilistic pursuit

• Florian Shkurti
• Gregory Dudek

We address the integrated planning and control problem that enables a single follower robot (the “photographer”) to maintain a moving target (the “subject”) in its field of view for as long as possible. We propose a real-time pursuit algorithm that seamlessly handles the often neglected, yet unavoidable, scenario in which the target escapes the follower's field of view; a scenario that simple, reactive controllers are ill-equipped to handle. Our algorithm aims to minimize the expected time until visual contact is re-established, which enables the photographer to track the subject for as long as possible, even in the presence of loss of visibility. At the core of our pursuit algorithm is an efficient method for sampling plausible trajectories from different homotopy classes. We do this by generating topologically distinct shortest paths by using the Voronoi diagram. We use these paths to make informed, model-based predictions of the likely future locations of the target, given a history of observations. Given these predictions, our algorithm produces pursuit trajectories that approximately minimize the expected time to recover visual contact. We show that constraining the predictive pursuit problem to the space of homotopy classes condenses the expanse of possibilities that our algorithm must consider, which enables target tracking in large occupancy grids, as opposed to many POMDP methods that are constrained to small environments. We benchmark the tracking behavior of our algorithm against the baseline of human subjects who performed the same set of pursuit tasks in simulation, as well as against two other pursuit algorithms that only take into account paths from a single homotopy class. We show that considering homotopy alternatives in 2D pursuit improves the tracking performance and that our algorithm does at least as well as humans in most pursuit scenarios.

IROS Conference 2017 Conference Paper

Underwater multi-robot convoying using visual tracking by detection

• Florian Shkurti
• Wei-Di Chang
• Peter Henderson 0002
• Md Jahidul Islam
• Juan Camilo Gamboa Higuera
• Jimmy Li 0001
• Travis Manderson
• Anqi Xu 0003

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i. e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot's independent motion.

IROS Conference 2016 Conference Paper

Fast and efficient rendezvous in street networks

• Malika Meghjani
• Sandeep Manjanna
• Gregory Dudek

We address the problem of rendezvous between two agents in urban street networks. Specifically, we consider the case where the agents have variable speeds and they need to schedule a rendezvous or a meeting under uncertainty in their travel times. Examples of such a scenario range from everyday life where two people would like to coordinate a meeting while going from office to home; to a futuristic case where automated taxis would like to meet each other for load balancing passengers. The scheduling for such scenarios can easily become challenging with uncertainties such as delayed departures, road blocks due to construction or traffic congestion. Any solution for such a task is required to minimize the waiting time and the planning overhead. In this paper, we propose an algorithm that optimizes the total travel time and the waiting time for two agents to complete their respective paths from start to rendezvous and from rendezvous to goal locations subject to delays along their paths. We validate our approach with a street network database which has a cost associated with every query made to the database server. Thus our algorithm intelligently optimizes for rendezvous trajectories that effectively mitigate the scourge of traffic delays, while simultaneously limiting the number of queries through careful analysis of the informative value of each potential query.

ICRA Conference 2016 Conference Paper

Learning to generalize 3D spatial relationships

• Jimmy Li 0001
• David Meger
• Gregory Dudek

This paper presents an approach to learn meaningful spatial relationships in an unsupervised fashion from the distribution of 3D object poses in the real world. Our approach begins by extracting an over-complete set of features to describe the relative geometry of two objects. Each relationship type is modeled using a relevance-weighted distance over this feature space. This effectively ignores irrelevant feature dimensions. Our algorithm RANSEM for determining subsets of data that share a relationship as well as the model to describe each relationship is based on robust sample-based clustering. This approach combines the search for consistent groups of data with the extraction of models that precisely capture the geometry of those groups. An iterative refinement scheme has shown to be an effective approach for finding concepts of differing degrees of geometric specificity. Our results show that the models learned by our approach correlate strongly with the English labels that have been given by a human annotator to a set of validation data drawn from the NYUv2 real-world Kinect dataset, demonstrating that these concepts can be automatically acquired given sufficient experience. Additionally, the results of our method significantly out-perform K-means, a standard baseline for unsupervised cluster extraction.

IROS Conference 2016 Conference Paper

Maintaining efficient collaboration with trust-seeking robots

• Anqi Xu 0003
• Gregory Dudek

In this work, we grant robot agents the capacity to sense and react to their human supervisor's changing trust state, as a means to maintain the efficiency of their collaboration. We propose the novel formulation of Trust-Aware Conservative Control (TACtiC), in which the agent alters its behaviors momentarily whenever the human loses trust. This trust-seeking robot framework builds upon an online trust inference engine and also incorporates an interactive behavior adaptation technique. We present end-to-end instantiations of trust-seeking robots for distinct task domains of aerial terrain coverage and interactive autonomous driving. Empirical assessments comprise a large-scale controlled interaction study and its extension into field evaluations with an autonomous car. These assessments substantiate the efficiency gains that trust-seeking agents bring to asymmetric human-robot teams.

IROS Conference 2016 Conference Paper

Multi-target rendezvous search

• Malika Meghjani
• Sandeep Manjanna
• Gregory Dudek

In this paper, we examine multi-target search, where one or more targets must be found by a moving robot. Given the target's initial probability distribution or the expected search region, we present an analysis of three search strategies - Global maxima search, Local maxima search, and Spiral search. We aim at minimizing the mean-time-to-find and maximizing the total probability of finding the target. This leads to two types of illustrative performance metrics: minimum time capture and guaranteed capture. We validate the search strategies with respect to these two performance metrics. In addition, we study the effect of different target distributions on the performance of the search strategies. We also consider the practical realization of the proposed algorithms for multi-target search. The search strategies are analytically evaluated, through simulations and illustrative deployments, in open-water with an Autonomous Surface Vehicle (ASV) and drifting sensor targets.

ICRA Conference 2015 Conference Paper

Autonomous gait selection for energy efficient walking

• Sandeep Manjanna
• Gregory Dudek

In this paper, we investigate the question of how a legged robot can walk efficiently by taking advantage of its ability to alter its gait as a function of statistical (large-scale) terrain properties. One of the contributions of this paper is the algorithm to achieve real-time terrain identification and autonomous gait adaptation on a legged robot. We approach this problem by first classifying the terrains based on their proprioceptive responses and identifying the terrain in real-time. Then we choose an optimal gait to best suit the identified terrain type. We exploit our recent findings regarding gaits, estimated from terrain-contact signatures, in order to obtain an optimized mapping between terrain signatures and terrain-specific gaits. We evaluate our algorithm on synthetic data, and real robot data collected on different terrains and naturally occurring terrain transitions. Another key contribution of this work is the statistical verification that precise gait selection can lead to energy savings in practice in legged robots. This assessment of energy efficiency, achieved by gait adaptation, is among the firsts of its kind in gait adaptation literature. We also present an analysis of the effect of terrain transition frequency on our gait adaptation algorithm. Our results are supported by validation using both synthetic data and field testing.

ICRA Conference 2015 Conference Paper

Learning legged swimming gaits from experience

• David Meger
• Juan Camilo Gamboa Higuera
• Anqi Xu 0003
• Philippe Giguère
• Gregory Dudek

We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies purely from experience data. Our method couples a state-of-the-art policy search technique with a family of periodic low-level controls that are well suited for underwater propulsion. We demonstrate the practical efficacy of tabula rasa learning, that is, learning without the use of any prior knowledge, of policies for a six-legged swimmer to carry out a variety of acrobatic maneuvers in three dimensional space. We also demonstrate informed learning that relies on simulated experience from a realistic simulator. In numerous cases, novel emergent gait behaviors have arisen from learning, such as the use of one stationary flipper to create drag while another oscillates to create thrust. Similar effective results have been demonstrated in under-actuated configurations, where as few as two flippers are used to maneuver the robot to a desired pose, or through an acrobatic motion such as a corkscrew. The success of our learning framework is assessed both in simulation and in the field using an underwater swimming robot.

IROS Conference 2015 Conference Paper

Robust environment mapping using flux skeletons

• Morteza Rezanejad
• Babak Samari
• Ioannis M. Rekleitis
• Kaleem Siddiqi
• Gregory Dudek

We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an on-line procedure which robustly computes a retraction of its boundaries. While such approaches are well known for their theoretical elegance, computing such representations in practice is complicated when the data is sparse and noisy. In this paper we present the online construction of a topological map and the implementation of a control law for guiding the robot to the nearest unexplored area. The proposed method operates by allowing the robot to localize itself on a partially constructed map, calculate a path to unexplored parts of the environment (frontiers), compute a robust terminating condition when the robot has fully explored the environment, and achieve loop closure detection. The proposed algorithm results in smooth safe paths for the robot's navigation needs. The presented approach is an any-time-algorithm which allows for the active creation of topological maps from laser-scan data, as it is being acquired. The resulting map is stable under variations to noise and the initial conditions. The key idea is the use of a flux-based skeletonization algorithm on the latest occupancy grid map. We also propose a navigation strategy based on a heuristic where the robot is directed towards nodes in the topological map that open to empty space. The method is evaluated on both synthetic data and in the context of active exploration using a Turtlebot 2. Our results demonstrate complete mapping of different environments with smooth topological abstraction without spurious edges.

IROS Conference 2014 Conference Paper

3D trajectory synthesis and control for a legged swimming robot

• David Meger
• Florian Shkurti
• David Cortés Poza
• Philippe Giguère
• Gregory Dudek

Inspection and exploration of complex underwater structures requires the development of agile and easy to program platforms. In this paper, we describe a system that enables the deployment of an autonomous underwater vehicle in 3D environments proximal to the ocean bottom. Unlike many previous approaches, our solution: uses oscillating hydrofoil propulsion; allows for stable control of the robot's motion and sensor directions; allows human operators to specify detailed trajectories in a natural fashion; and has been successfully demonstrated as a holistic system in the open ocean near both coral reefs and a sunken cargo ship. A key component of our system is the 3D control of a hexapod swimming robot, which can move the vehicle through agile sequences of orientations despite challenging marine conditions. We present two methods to easily generate robot trajectories appropriate for deployments in close proximity to challenging contours of the sea floor. Both offline recording of trajectories using augmented reality and online placement of fiducial tags in the marine environment are shown to have desirable properties, with complementary strengths and weaknesses. Finally, qualitative and quantitative results of the 3D control system are presented.

ICRA Conference 2014 Conference Paper

Adaptive Parameter EXploration (APEX): Adaptation of robot autonomy from human participation

• Anqi Xu 0003
• Arnold Kalmbach
• Gregory Dudek

The problem of Adaptation from Participation (AfP) aims to improve the efficiency of a human-robot team by adapting a robot's autonomous systems and behaviors based on command-level input from a human supervisor. As a solution to AfP, the Adaptive Parameter EXploration (APEX) algorithm continuously explores the space of all possible parameter configurations for the robot's autonomous system in an online and anytime manner. Guided by information deduced from the human's latest intervening commands, APEX is capable of adapting an arbitrary robot system to dynamic changes in task objectives and conditions during a session. We explore this framework within visual navigation contexts where the humanrobot team is tasked with covering or patrolling over multiple terrain boundaries such as coastlines and roads. We present empirical evaluations of two separate APEX-enabled systems: the first, deployed on an aerial robot within a controlled environment, and the second, on a wheeled robot operating within a challenging university campus setting.

ICRA Conference 2014 Conference Paper

Curiosity based exploration for learning terrain models

• Yogesh A. Girdhar
• David Whitney
• Gregory Dudek

We present a robotic exploration technique in which the goal is to learn a visual model that can be used to distinguish between different terrains and other visual components in an unknown environment. We use ROST, a realtime online spatiotemporal topic modeling framework to model these terrains using the observations made by the robot, and then use an information theoretic path planning technique to define the exploration path. We conduct experiments with aerial view and underwater datasets with millions of observations and varying path lengths, and find that paths that are biased towards locations with high topic perplexity produce better terrain models with high discriminative power.

ICRA Conference 2014 Conference Paper

Maximizing visibility in collaborative trajectory planning

• Florian Shkurti
• Gregory Dudek

In this paper we address the issue of coordinating the trajectories of two collaborating robots in environments with obstacles so that visibility between them is maximized in the presence of competing constraints. Specifically, we examine the problem of allowing one robot (the “photographer”) to follow another robot (“the subject”) through a planar environment while maintaining visual contact to the maximum degree consistent with an efficient traversal. This problem has numerous applications, for instance in scenarios where communication between robots requires line-of-sight. We formalize this problem in the context of centralized kinodynamic planning and we present solutions based on the asymptotically optimal sampling-based RRT* planner. We discuss connections to the traditional formulation of pursuit-evasion games where the analysis typically ends the moment the evader manages to escape the pursuer's visibility region. We also illustrate types of environments and other conditions under which allowing the pair of robots to break the line-of-sight is a better option than always requiring the presence of visual contact.

ICRA Conference 2014 Conference Paper

Multi-agent rendezvous on street networks

• Malika Meghjani
• Gregory Dudek

In this paper we present an algorithm for finding a distance optimal rendezvous location with respect to both initial and target locations of the mobile agents. These agents can be humans or robots, who need to meet and split while performing a collaborative task. Our aim is to embed the meeting process within a background activity such that the agents travel through the rendezvous location while taking the shortest paths to their respective target locations. We analyze this problem in a street network scenario with two agents who are given their individual scheduled routes to complete with an underlying common goal. The agents are allowed to select any combination of the waypoints along their routes as long as they travel the shortest path and pass through the same potential rendezvous location. The total number of path combinations that the agents need to evaluate for the shortest path increases rapidly with the number of waypoints along their routes. We address this computational cost by proposing a combination of Euclidean and street network distances for a trade-off between the number of queries and a distance optimal solution.

ICRA Conference 2013 Conference Paper

Fair subdivision of multi-robot tasks

• Juan Camilo Gamboa Higuera
• Gregory Dudek

We study the problem of distributing a single global task between a group of heterogeneous robots. We view this problem as a fair division game. In this setting, every robot defines a preference function over parts of the task according to its sensing and motion capabilities. These preferences are described by density functions over the task. With such interpretation, we want to find an allocation of the global task that maximizes the probability of task completion. We first formulate the task distribution problem as a fair subdivision problem and provide a centralized algorithm to compute the allocations for each robot. We provide a complexity analysis and computational results of the algorithm.

IROS Conference 2013 Conference Paper

Ninja legs: Amphibious one degree of freedom robotic legs

• Bir Bikram Dey
• Sandeep Manjanna
• Gregory Dudek

In this paper we propose a design of a class of robotic legs (known as “Ninja legs”) that enable amphibious operation, both walking and swimming, for use on a class of hexapod robots. Amphibious legs equip the robot with a capability to explore diverse locations in the world encompassing both those that are on the ground as well as underwater. In this paper we work with a hexapod robot of the Aqua vehicle family (based on a body plan first developed by Buehler et al. [1]), which is an amphibious robot that employs legs for amphibious locomotion. Many different leg designs have been previously developed for Aqua-class vehicles, including both robust all-terrain legs for walking, and efficient flippers for swimming. But the walking legs have extremely poor thrust for swimming and the flippers are completely unsuitable for terrestrial operations. In this work we propose a single leg design with the advantages of both the walking legs and the swimming flippers. We design a cage-like circular enclosure for the flippers in order to protect the flippers during terrestrial operations. The enclosing structure also plays the role of the walking legs for terrestrial locomotion. The circular shape of the enclosure, as well, has the advantages of an offset wheel. We evaluate the performance of our design for terrestrial mobility by comparing the power efficiency and the physical speed of the robot equipped with the newly designed legs against that with the walking legs which are semi-circular in shape. The swimming performance is examined by measuring the thrust generated by newly designed legs and comparing the same with the thrust generated by the swimming flippers. In the field, we also verified that these legs are suitable for swimming through moderate surf, walking through the breakers on a beach (and thus through slurry), and onto wet and dry sand.

ICRA Conference 2013 Conference Paper

On the complexity of searching for an evader with a faster pursuer

• Florian Shkurti
• Gregory Dudek

In this paper we examine pursuit-evasion games in which the pursuer has higher speed than the evader. This scenario is motivated by visibility-based pursuit-evasion problems, particularly by the question of what happens when the pursuer loses visual track of the moving evader. In these cases the pursuer has two options for recovering visual contact with the evader: to perform search over the possible locations where the evader might be moving, or to clear the environment, in other words to progressively search it without allowing the evader to move into locations that have already been cleared. It has been shown that in sufficiently complex environments a single pursuer having the same speed as the evader cannot clear the environment. In this work we prove that computing the minimum speed which enables a faster pursuer to clear a graph environment is NP-hard. In light of this result we provide an experimental comparison of randomized and deterministic search strategies on planar graphs, which has practical significance in search and rescue settings.

ICRA Conference 2013 Conference Paper

Unsupervised environment recognition and modeling using sound sensing

• Arnold Kalmbach
• Yogesh A. Girdhar
• Gregory Dudek

We discuss the problem of automatically discovering different acoustic regions in the world, and then labeling the trajectory of a robot using these region labels. We use quantized Mel Frequency Cepstral Coefficients (MFCC) as low level features, and a temporally smoothed variant of Latent Dirichlet Allocation (LDA) to compute both the region models, and most likely region labels associated with each time step in the robot's trajectory. We validate our technique by showing results from two datasets containing sound recorded from 51 and 43 minute long trajectories through downtown Montreal and the McGill University campus. Our preliminary experiments indicate that the regions discovered by the proposed technique correlate well with ground truth, labeled by a human expert.

ICRA Conference 2012 Conference Paper

Efficient on-line data summarization using extremum summaries

• Yogesh A. Girdhar
• Gregory Dudek

We are interested in the task of online summarization of the data observed by a mobile robot, with the goal that these summaries could be then be used for applications such as surveillance, identifying samples to be collected by a planetary rover, and site inspections to detect anomalies. In this paper, we pose the summarization problem as an instance of the well known k-center problem, where the goal is to identify k observations so that the maximum distance of any observation from a summary sample is minimized. We focus on the online version of the summarization problem, which requires that the decision to add an incoming observation to the summary be made instantaneously. Moreover, we add the constraint that only a finite number of observed samples can be saved at any time, which allows for applications where the selection of a sample is linked to a physical action such as rock sample collection by a planetary rover. We show that the proposed online algorithm has performance comparable to the offline algorithm when used with real world data.

IROS Conference 2012 Conference Paper

Multi-domain monitoring of marine environments using a heterogeneous robot team

• Florian Shkurti
• Anqi Xu 0003
• Malika Meghjani
• Juan Camilo Gamboa Higuera
• Yogesh A. Girdhar
• Philippe Giguère
• Bir Bikram Dey
• Jimmy Li 0001

In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.

IROS Conference 2012 Conference Paper

Multi-robot exploration and rendezvous on graphs

• Malika Meghjani
• Gregory Dudek

We address the problem of arranging a meeting (or rendezvous) between two or more robots in an unknown bounded topological environment, starting at unknown locations, without any communication. The goal is to rendezvous in minimum time such that the robots can share resources for performing any global task. We specifically consider a global exploration task executed by two or more robots. Each robot explores the environment simultaneously, for a specified time, then selects potential rendezvous locations, where it expects to find other robots, and visits them. We propose a ranking criterion for selecting the order in which potential rendezvous locations will be visited. This ranking criterion associates a cost for visiting a rendezvous location and gives an expected reward of finding other agents. We evaluate the time taken to rendezvous by varying a set of conditions including: world size, number of robots, starting location of each robot and the presence of sensor noise. We present simulation results to quantify the effect of the aforementioned factors on the rendezvous time.

ICRA Conference 2012 Conference Paper

Trust-driven interactive visual navigation for autonomous robots

• Anqi Xu 0003
• Gregory Dudek

We describe a model of “trust” in human-robot systems that is inferred from their interactions, and inspired by similar concepts relating to trust among humans. This computable quantity allows a robot to estimate the extent to which its performance is consistent with a human's expectations, with respect to task demands. Our trust model drives an adaptive mechanism that dynamically adjusts the robot's autonomous behaviors, in order to improve the efficiency of the collaborative team. We illustrate this trust-driven methodology through an interactive visual robot navigation system. This system is evaluated through controlled user experiments and a field demonstration using an aerial robot.

ICRA Conference 2011 Conference Paper

Graphical State Space Programming: A visual programming paradigm for robot task specification

• Jimmy Li 0001
• Anqi Xu 0003
• Gregory Dudek

We describe a framework that combines a software development paradigm, a software visualization technique, and a tool for robot programming. This infrastructure is called “Graphical State Space Programming” (GSSP), and allows robot application programs to be decomposed and visualized within state-dependent views. Our approach simplifies and expedites the programming process for robot routines and behaviors, and we examine the performance improvement that ensues through a set of controlled user studies. The usability and effectiveness of GSSP are also illustrated using a field demonstration with an aerial robotic vehicle.

IROS Conference 2011 Conference Paper

MARE: Marine Autonomous Robotic Explorer

• Yogesh A. Girdhar
• Anqi Xu 0003
• Bir Bikram Dey
• Malika Meghjani
• Florian Shkurti
• Ioannis M. Rekleitis
• Gregory Dudek

We present MARE, an autonomous airboat robot that is suitable for exploration-oriented tasks, such as inspection of coral reefs and shallow seabeds. The combination of this platform's particular mechanical properties and its powerful software framework enables it to function in a multitude of potential capacities, including autonomous surveillance, mapping, and search operations. In this paper we describe two different exploration strategies and their implementation using the MARE platform. First, we discuss the application of an efficient coverage algorithm, for the purpose of achieving systematic exploration of a known and bounded environment. Second, we present an exploration strategy driven by surprise, which steers the robot on a path that might lead to potentially surprising observations.

ICRA Conference 2011 Conference Paper

Offline navigation summaries

• Yogesh A. Girdhar
• Gregory Dudek

In this paper we focus on the task of summarizing observations made by a mobile robot on a trajectory. A navigation summary is the synopsis of these observations. We pose the problem of generating navigation summaries as a sampling problem. The goal is to select a few samples from the set of all observations, which are characteristic of the environment, and capture its mean properties and surprises. We define the surprise score of an observation as its distance to the closest sample in the summary. Hence, an ideal summary is defined to have a low mean and a low max surprise score, measured over all the observations. We present three different strategies for solving this sampling problem. Of these, we show that the kCover sampling algorithm produces summaries with low mean and max surprise scores; even in the presence of noise. These results are demonstrated on datasets acquired in different robotics context.

IROS Conference 2011 Conference Paper

State estimation of an underwater robot using visual and inertial information

• Florian Shkurti
• Ioannis M. Rekleitis
• Milena Scaccia
• Gregory Dudek

This paper presents an adaptation of a vision and inertial-based state estimation algorithm for use in an underwater robot. The proposed approach combines information from an Inertial Measurement Unit (IMU) in the form of linear accelerations and angular velocities, depth data from a pressure sensor, and feature tracking from a monocular downward facing camera to estimate the 6DOF pose of the vehicle. To validate the approach, we present extensive experimental results from field trials conducted in underwater environments with varying lighting and visibility conditions, and we demonstrate successful application of the technique underwater.

ICRA Conference 2011 Conference Paper

Towards quantitative modeling of task confirmations in human-robot dialog

• Junaed Sattar
• Gregory Dudek

We present a technique for robust human-robot interaction taking into consideration uncertainty in input and task execution costs incurred by the robot. Specifically, this research aims to quantitatively model confirmation feedback, as required by a robot while communicating with a human operator to perform a particular task. Our goal is to model human-robot interaction from the perspective of risk minimization, taking into account errors in communication, “risk” involved in performing the required task, and task execution costs. Given an input modality with non-trivial uncertainty, we calculate the cost associated with performing the task specified by the user, and if deemed necessary, ask the user for confirmation. The estimated task cost and the uncertainty measure are given as input to a Decision Function, the output of which is then used to decide whether to execute the task, or request clarification from the user. We test our system through human-interface experiments, based on a framework custom-designed for our family of amphibious robots, and demonstrate the utility of the framework in the presence of large task costs and uncertainties. We also present qualitative results of our algorithm from field trials of our robots in both open- and closed-water environments.

IROS Conference 2010 Conference Paper

A vision-based boundary following framework for aerial vehicles

• Anqi Xu 0003
• Gregory Dudek

We present an integration of classical computer vision techniques to achieve real-time autonomous steering of an unmanned aircraft along the boundary of different regions. Using an unified conceptual framework, we illustrate solutions for tracking coastlines and for following roads surrounded by forests. In particular, we exploit color and texture properties to differentiate between region types in the aforementioned domains. The performance of our system is evaluated using different experimental approaches, which includes a fully automated in-field flight over a 1km coastline trajectory.

ICRA Conference 2010 Conference Paper

Graphical state-space programmability as a natural interface for robotic control

• Junaed Sattar
• Anqi Xu 0003
• Gregory Dudek
• Gabriel Charette

We present an interface for controlling mobile robots that combines aspects of graphical trajectory specification and state-based programming. This work is motivated by common tasks executed by our underwater vehicles, although we illustrate a mode of interaction that is applicable to mobile robotics in general. The key aspect of our approach is to provide an intuitive linkage between the graphical visualization of regions of interest in the environment, and activities relevant to these regions. In addition to introducing this novel programming paradigm, we also describe the associated system architecture developed on-board our amphibious robot. We then present a user interaction study that illustrates the benefits in usability of our graphical interface, compared to conventionally established programming techniques.

ICRA Conference 2010 Conference Paper

Online navigation summaries

• Yogesh A. Girdhar
• Gregory Dudek

Our objective is to find a small set of images that summarize a robot's visual experience along a path. We present a novel on-line algorithm for this task. This algorithm is based on a new extension to the classical Secretaries Problem. We also present an extension to the idea of Bayesian Surprise, which we then use to measure the fitness of an image as a summary image.

IROS Conference 2010 Conference Paper

ONSUM: A system for generating online navigation summaries

• Yogesh A. Girdhar
• Gregory Dudek

We propose an algorithm for generating navigation summaries. Navigation summaries are a specialization of video summaries, where the focus is on video collected by a mobile robot, on a specified trajectory. We are interested in finding a few images that epitomize the visual experience of a robot as it traverses a terrain. This paper presents a novel approach to generating summaries in form of a set of images, where the decision to include the image in the summary set is made online. Our focus is on the case where the number of observations is infinite or unknown, but the size of the desired summary is known. Our strategy is to consider the images in the summary set as the prior hypothesis of the appearance of the world, and then use Set Theoretic Surprise to compute the novelty of an observed image. If the novelty is above a threshold, then we accept the image. We discuss different criterion for setting this threshold. Online nature of our approach allows for several interesting applications such as coral reef inspection, surveying, and surveillance.

ICRA Conference 2009 Conference Paper

Inferring a probability distribution function for the pose of a sensor network using a mobile robot

• David Meger
• Dimitri Marinakis
• Ioannis M. Rekleitis
• Gregory Dudek

In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. We present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing Rao-Blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. Our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. We demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware.

ICRA Conference 2009 Conference Paper

Robust servo-control for underwater robots using banks of visual filters

• Junaed Sattar
• Gregory Dudek

We present an application of machine learning to the semi-automatic synthesis of robust servo-trackers for underwater robotics. In particular, we investigate an approach based on the use of Boosting for robust visual tracking of color objects in an underwater environment. To this end, we use AdaBoost, the most common variant of the Boosting algorithm, to select a number of low-complexity but moderately accurate color feature trackers and we combine their outputs. The novelty of our approach lies in the design of this family of weak trackers, which enhances a straightforward color segmentation tracker in multiple ways. From a large and diverse family of possible filters, we select a small subset that optimizes the performance of our trackers. The tracking process applies these trackers on the input video frames, and the final tracker output is chosen based on the weights of the final array of trackers. By using computationally inexpensive, but somewhat accurate trackers as members of the ensemble, the system is able to run at quasi real-time, and thus, is deployable on-board our underwater robot. We present quantitative cross-validation results of our spatio-chromatic visual tracker, and conclude by pointing out some difficulties faced and subsequent shortcomings in the experiments we performed, along with directions of future research in the area of ensemble tracking in real-time.

ICRA Conference 2009 Conference Paper

Surface identification using simple contact dynamics for mobile robots

• Philippe Giguère
• Gregory Dudek

This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on analyzing the tip acceleration patterns induced in a metallic rod, dragged along a surface that is to be identified. Eight features in time and frequency domains are used for classification. Results show that for ten type of indoor and outdoor surfaces, reliable identification can be achieved (90. 0 and 94. 6 percent for a 1 and 4 seconds time-window, respectively), using a non-sophisticated classifier (artificial neural network). Demonstration is done on how such a sensor and a simple control strategy can be used to guide a blind robot, using a simulation and a real differential drive robot.

ICRA Conference 2008 Conference Paper

A natural gesture interface for operating robotic systems

• Anqi Xu 0003
• Gregory Dudek
• Junaed Sattar

A gesture-based interaction framework is presented for controlling mobile robots. This natural interaction paradigm has few physical requirements, and thus can be deployed in many restrictive and challenging environments. We present an implementation of this scheme in the control of an underwater robot by an on-site human operator. The operator performs discrete gestures using engineered visual targets, which are interpreted by the robot as parametrized actionable commands. By combining the symbolic alphabets resulting from several visual cues, a large vocabulary of statements can be produced. An iterative closest point algorithm is used to detect these observed motions, by comparing them with an established database of gestures. Finally, we present quantitative data collected from human participants indicating accuracy and performance of our proposed scheme.

IROS Conference 2008 Conference Paper

Enabling autonomous capabilities in underwater robotics

• Junaed Sattar
• Gregory Dudek
• Olivia Chiu
• Ioannis M. Rekleitis
• Philippe Giguère
• Alec Mills
• Nicolas Plamondon
• Chris Prahacs

Underwater operations present unique challenges and opportunities for robotic applications. These can be attributed in part to limited sensing capabilities, and to locomotion behaviours requiring control schemes adapted to specific tasks or changes in the environment. From enhancing teleoperation procedures, to providing high-level instruction, all the way to fully autonomous operations, enabling autonomous capabilities is fundamental for the successful deployment of underwater robots. This paper presents an overview of the approaches used during underwater sea trials in the coral reefs of Barbados, for two amphibious mobile robots and a set of underwater sensor nodes. We present control mechanisms used for maintaining a preset trajectory during enhanced teleoperations and discuss their experimental results. This is followed by a discussion on amphibious data gathering experiments conducted on the beach. We then present a tetherless underwater communication approach based on pure vision for high-level control of an underwater vehicle. Finally the construction details together with preliminary results from a set of distributed underwater sensor nodes are outlined.

IROS Conference 2008 Conference Paper

Heuristic search planning to reduce exploration uncertainty

• David Meger
• Ioannis M. Rekleitis
• Gregory Dudek

The path followed by a mobile robot while mapping an environment (i. e. an exploration trajectory) plays a large role in determining the efficiency of the mapping process and the accuracy of any resulting metric map of the environment. This paper examines some important aspects of path planning in this context: the trade-offs between the speed of the exploration process versus the accuracy of resulting maps; and alternating between exploration of new territory and planning through known maps. The resulting motion planning strategy and associated heuristic are targeted to a robot building a map of an environment assisted by a Sensor Network composed of uncalibrated monocular cameras. An adaptive heuristic exploration strategy based on A * search over a combined distance and uncertainty cost function allows for adaptation to the environment and improvement in mapping accuracy. We assess the technique using an illustrative experiment in a real environment and a set of simulations in a parametric family of idealized environments.

IJCAI Conference 2007 Conference Paper

• Dimitri Marinakis
• Gregory Dudek

In this paper we address the problem of inferring the topology, or inter-node navigability, of a sensor network given non-discriminating observations of activity in the environment. By exploiting motion present in the environment, our approach is able to recover a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We employ a reasoning system made up of a stochastic Expectation Maximization algorithm and a higher level search strategy employing the principle of Occam's Razor to look for the simplest solution explaining the data. The technique is assessed through numerical simulations and experiments conducted on a real sensor network.

ICRA Conference 2007 Conference Paper

A Visual Language for Robot Control and Programming: A Human-Interface Study

• Gregory Dudek
• Junaed Sattar
• Anqi Xu 0003

We describe an interaction paradigm for controlling a robot using hand gestures. In particular, we are interested in the control of an underwater robot by an on-site human operator. Under this context, vision-based control is very attractive, and we propose a robot control and programming mechanism based on visual symbols. A human operator presents engineered visual targets to the robotic system, which recognizes and interprets them. This paper describes the approach and proposes a specific gesture language called "RoboChat". RoboChat allows an operator to control a robot and even express complex programming concepts, using a sequence of visually presented symbols, encoded into fiducial markers. We evaluate the efficiency and robustness of this symbolic communication scheme by comparing it to traditional gesture-based interaction involving a remote human operator

AAAI Conference 2007 Conference Paper

Topological Mapping with Weak Sensory Data

• Gregory Dudek

In this paper, we consider the exploration of topological environments by a robot with weak sensory capabilities. We assume only that the robot can recognize when it has reached a vertex, and can assign a cyclic ordering to the edges leaving a vertex with reference to the edge it arrived from. Given this limited sensing capability, and without the use of any markers or additional information, we will show that the construction of a topological map is still feasible. This is accomplished through both the exploration strategy which is designed to reveal model inconsistencies and by a search process that maintains a bounded set of believable world models throughout the exploration process. Plausible models are selected through the use of a ranking heuristic function based on the principle of Occam’s Razor. We conclude with numerical simulations demonstrating the performance of the algorithm.

IROS Conference 2007 Conference Paper

Where is your dive buddy: tracking humans underwater using spatio-temporal features

• Junaed Sattar
• Gregory Dudek

We present an algorithm for underwater robots to track mobile targets, and specifically human divers, by detecting periodic motion. Periodic motion is typically associated with propulsion underwater and specifically with the kicking of human swimmers. By computing local amplitude spectra in a video sequence, we find the location of a diver in the robot's field of view. We use the Fourier transform to extract the responses of varying intensities in the image space over time to detect characteristic low frequency oscillations to identify an undulating flipper motion associated with typical gaits. In case of detecting multiple locations that exhibit large low-frequency energy responses, we combine the gait detector with other methods to eliminate false detections. We present results of our algorithm on open-ocean video footage of swimming divers, and also discuss possible extensions and enhancements of the proposed approach for tracking other objects that exhibit low- frequency oscillatory motion.

ICRA Conference 2006 Conference Paper

A Practical Algorithm for Network Topology Inference

• Dimitri Marinakis
• Gregory Dudek

When a network of robots or static sensors is emplaced in an environment, the spatial relationships between the sensing units must be inferred or computed for most key applications. In this paper we present a Monte Carlo expectation maximization algorithm for recovering the connectivity information (i. e. topological map) of a network using only detection events from deployed sensors. The technique is based on stochastically reconstructing samples of plausible agent trajectories allowing for the possibility of transitions to and from sources and sinks in the environment. We demonstrate robustness to sensor error and non-trivial patterns of agent motion. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We conclude with results from numerical simulations and an experiment conducted with a heterogeneous sensor network

IROS Conference 2006 Conference Paper

Characterization and Modeling of Rotational Responses for an Oscillating Foil Underwater Robot

• Philippe Giguère
• Chris Prahacs
• Gregory Dudek

In order to better understand the behavior of the underwater robot developed at our laboratory, a simple but relatively good model of the underwater behavior of the robot had to be developed. In order to be useful for model-based control techniques onboard the robot, the model had to have low computing requirements, yet be complex enough to capture the transient response of the robot. To achieve this, a system identification approach was taken by first capturing the robot response to various inputs, and then matching them to a simple model

ICRA Conference 2006 Conference Paper

On the Performance of Color Tracking Algorithms for Underwater Robots under Varying Lighting and Visibility

• Junaed Sattar
• Gregory Dudek

We consider the use of visual target tracking for autonomous steering of an underwater robot. In this context, we consider a performance comparison for three key visual tracking algorithms used for servo control. We present a comparative study of the performance in underwater environments of three tracking algorithms that are widely used in vision applications. Variations in illumination, suspended particles and a resulting reduction in visibility hinders vision systems from performing satisfactorily in marine environments; at least not as well as they do in terrestrial (Le. non-underwater) surroundings. Our work focuses on quantitatively measuring the performance of three color-based tracking algorithms- color blob tracker, color histogram tracker and mean-shift tracker, in tracking objects underwater in different levels lighting and visibility. We also present results demonstrating the effect of suspended particles underwater, and in conclusion we summarize the three tracking algorithms by comparing their pros and cons

ICAPS Conference 2006 Conference Paper

RRT-Plan: A Randomized Algorithm for STRIPS Planning

• Daniel Burfoot
• Joelle Pineau
• Gregory Dudek

We propose a randomized STRIPS planning algorithm called RRT-Plan. This planner is inspired by the idea of Rapidly exploring Random Trees, a concept originally designed for use in continuous path planning problems. Issues that arise in the conversion of RRTs from continuous to discrete spaces are discussed, and several additional mechanisms are proposed to improve performance. Our experimental results indicate that RRT-Plan is competitive with the state of the art in STRIPS planning.

IROS Conference 2005 Conference Paper

A visual servoing system for an aquatic swimming robot

• Junaed Sattar
• Philippe Giguère
• Gregory Dudek
• Chris Prahacs

This paper describes a visual servoing system for an underwater legged robotic system named AQUA and initial experiments with the system performed in the open sea. A large class of significant applications can be leveraged by allowing such a robot to follow a diver or some other moving target. The robot uses a suite of sensing technologies, primarily based on computer vision, to allow it to navigate in shallow-water environments. The visual servoing system described here allows the robot to track and follow a given target underwater. The servo package is made up of two distinct parts: a tracker and a feedback controller. The system has been evaluated in the sea water and under natural lighting conditions. The servo system has been tested underwater, and with minor modifications, the system can be used while the robot is walking on the ground as well.

IROS Conference 2005 Conference Paper

A visually guided swimming robot

• Gregory Dudek
• Michael Jenkin
• Chris Prahacs
• Andrew Hogue
• Junaed Sattar
• Philippe Giguère
• Andrew German
• Hui Liu

We describe recent results obtained with AQUA, a mobile robot capable of swimming, walking and amphibious operation. Designed to rely primarily on visual sensors, the AQUA robot uses vision to navigate underwater using servo-based guidance, and also to obtain high-resolution range scans of its local environment. This paper describes some of the pragmatic and logistic obstacles encountered, and provides an overview of some of the basic capabilities of the vehicle and its associated sensors. Moreover, this paper presents the first ever amphibious transition from walking to swimming.

IROS Conference 2005 Conference Paper

Automated calibration of a camera sensor network

• Ioannis M. Rekleitis
• Gregory Dudek

In this paper we present a new approach for the online calibration of a camera sensor network. This is the first step towards fully exploiting the potential for collaboration between mobile robots and static sensors sharing the same network. In particular we propose an approach for extracting the 3D pose of each camera in a common reference frame, with the help of a mobile robot. The camera poses can then be used to further refine the robot pose or to perform other tracking tasks. The analytical formulation of the problem of pose recovery is presented together with experimental results of a six node sensor network in different configurations.

ICRA Conference 2005 Conference Paper

Learning Sensor Network Topology through Monte Carlo Expectation Maximization

• Dimitri Marinakis
• Gregory Dudek
• David J. Fleet

We consider the problem of inferring sensor positions and a topological (i. e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In this way, without prior knowledge of the environment nor the exact position of sensors within the environment, one can infer the topology of the environment, and common traffic patterns within it. In particular, we consider sensors stationed at the junctions of the hallways of a large building. We infer the sensor connectivity graph and the travel times between sensors (and hence the hallway topology) from the sequence of events caused by unlabeled agents (i. e. people) passing within view of the different sensors. We do this based on a first-order semi-Markov model of the agent's behavior. The paper describes a problem formulation and proposes a stochastic algorithm for its solution. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic patterns. We conclude with results from numerical simulations

ICRA Conference 2005 Conference Paper

Minimum Distance Localization for a Robot with Limited Visibility

• Malvika Rao
• Gregory Dudek
• Sue Whitesides

Minimum distance localization is the problem of finding the shortest possible path for a robot to eliminate ambiguity regarding its position in the environment. We consider the problem of minimum distance localization in self-similar environments, where the robot's sensor has limited visibility, and describe two randomized algorithms that solve the problem. Our algorithms reduce the risk of requiring impractical observations and solve the problem without excessive computation. Our results are validated using numerical simulations.

AAAI Conference 2004 Conference Paper

Analogical Path Planning

• Saul Simhon
• Gregory Dudek

We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred motions for a moving robot. Our system learns constraints and preference biases on a robot’s motion from examples, and then synthesizes behaviors that satisfy these constraints. This behavior can encompass motions that satisfy diverse requirements such as a sweep pattern for floor coverage, or, in particular in our experiments, satisfy restrictions on the robot’s physical capabilities such as restrictions on its turning radius. Given an approximate path that may not satisfy the required conditions, our system computes a refined path that satisfies the constraints and also avoids obstacles. Our approach is based on a Bayesian framework for combining a prior probability distribution on the trajectory with environmental constraints. The prior distribution is generated by decoding a Hidden Markov Model, which is itself is trained over a particular set of preferred motions. Environmental constraints are modeled using a potential field over the configuration space. This paper poses the requisite theoretical framework and demonstrates its effectiveness with several examples.

IROS Conference 2004 Conference Paper

AQUA: an aquatic walking robot

• Christina Georgiades
• Andrew German
• Andrew Hogue
• Hui Liu
• Chris Prahacs
• Arlene Ripsman
• Robert Sim
• Luz Abril Torres-Méndez

This paper describes an underwater walking robotic system being developed under the name AQUA, the goals of the AQUA project, the overall hardware and software design, the basic hardware and sensor packages that have been developed, and some initial experiments. The robot is based on the RHex hexapod robot and uses a suite of sensing technologies, primarily based on computer vision and INS, to allow it to navigate and map clear shallow-water environments. The sensor-based navigation and mapping algorithms are based on the use of both artificial floating visual and acoustic landmarks as well as on naturally occurring underwater landmarks and trinocular stereo.

IROS Conference 2004 Conference Paper

Learning generative models of invariant features

• Robert Sim
• Gregory Dudek

We present a method for learning a set of models of visual features which are invariant to scale and translation in the image domain. The models are constructed by first applying the scale-invariant feature transform (SIFT) to a set of training images, and matching the extracted features across the images, followed by learning the pose-dependent behavior of the features. The modeling process avoids assumptions with respect to scene and imaging geometry, but rather learns the direct mapping from camera pose to feature observation. Such models are useful for applications to robotic tasks, such as localization, as well as visualization tasks. We present the model learning framework, and experimental results illustrating the success of the method for learning models that are useful for robot localization.

ICRA Conference 2004 Conference Paper

Online Control Policy Optimization for Minimizing Map Uncertainty during Exploration

• Robert Sim
• Gregory Dudek
• Nicholas Roy

Tremendous progress has been made recently in simultaneous localization and mapping of unknown environments. Using sensor and odometry data from an exploring mobile robot, it has become much easier to build high-quality globally consistent maps of many large, real-world environments. To date, however, relatively little attention has been paid to the controllers used to build these maps. Existing exploration strategies usually attempt to cover the largest amount of unknown space as quickly as possible. Few strategies exist for building the most reliable map possible, but the particular control strategy can have a substantial impact on the quality of the resulting map. In this paper, we devise a control algorithm for exploring unknown space that explicitly tries to build as large a map as possible while maintaining as accurate a map as possible. We make use of a parameterized class of spiral trajectory policies, choosing a new parameter setting at every time step to maximize the expected reward of the policy. We do this in the context of building a visual map of an unknown environment, and show that our strategy leads to a higher accuracy map faster than other candidate controllers, including any single choice in our policy class.

AAAI Conference 2004 Conference Paper

Reconstruction of 3D Models from Intensity Images and Partial Depth

• Luz A. Torres-Méndez
• Gregory Dudek

This paper addresses the probabilistic inference of geometric structures from images. Specifically, of synthesizing range data to enhance the reconstruction of a 3D model of an indoor environment by using video images and (very) partial depth information. In our method, we interpolate the available range data using statistical inferences learned from the concurrently available video images and from those (sparse) regions where both range and intensity information is available. The spatial relationships between the variations in intensity and range can be efficiently captured by the neighborhood system of a Markov Random Field (MRF). In contrast to classical approaches to depth recovery (i. e. stereo, shape from shading), we can afford to make only weak prior assumptions regarding specific surface geometries or surface reflectance functions since we compute the relationship between existing range data and the images we start with. Experimental results show the feasibility of our method.

AAAI Conference 2004 Conference Paper

Self-Organizing Visual Maps

• Robert Sim
• Gregory Dudek

This paper deals with automatically learning the spatial distribution of a set of images. That is, given a sequence of images acquired from well-separated locations, how can they be arranged to best explain their genesis? The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem where only limited prior odometric information is available, employing a feature-based method derived from a probabilistic pose estimation framework. Initially, a set of visual features is selected from the images and correspondences are found across the ensemble. The images are then localized by first assembling the small subset of images for which odometric confidence is high, and sequentially inserting the remaining images, localizing each against the previous estimates, and taking advantage of any priors that are available. We present experimental results validating the approach, and demonstrating metrically and topologically accurate results over two large image ensembles. Finally, we discuss the results, their relationship to the autonomous exploration of an unknown environment, and their utility for robot localization and navigation.

IROS Conference 2004 Conference Paper

Statistical inference and synthesis in the image domain for mobile robot environment modeling

• Luz Abril Torres-Méndez
• Gregory Dudek

We address the problem of computing dense range maps of indoor locations using only intensity images and partial depth. We allow a mobile robot to navigate the environment, take some pictures and few range data. Our method is based on interpolating the existing range data using statistical inferences learned from the available intensity image and from those (sparse) regions where both range and intensity information is present. The spatial relationships between the variations in intensity and range can be efficiently captured by the neighborhood system of a Markov random field (MRF). In contrast to classical approaches to depth recovery (i. e. stereo, shape from shading), we can afford to make only weak assumptions regarding specific surface geometries or surface reflectance functions since we compute the relationship between existing range data and the images we started with. Experimental results show the feasibility of our method.

IJCAI Conference 2003 Conference Paper

Comparing image-based localization methods

• Robert Sim
• Gregory Dudek

This paper compares alternative approaches to pose estimation using visual cues from the environment. We examine approaches that derive pose estimates from global image properties, such as principal components analysis (PCA) versus from local image properties, commonly referred to as landmarks. We also consider the failure-modes of the different methods. Our work is validated with experimental results.

IROS Conference 2003 Conference Paper

Effective exploration strategies for the construction of visual maps

• Robert Sim
• Gregory Dudek

We consider the effect of exploration policy in the context of the autonomous construction of a visual map of an unknown environment. Like other concurrent mapping and localization (CML) tasks, odometric uncertainty poses the problem of introducing distortions into the map which are difficult to correct without costly on-line or post-processing algorithms. Our problem is further compounded by the implicit nature of the visual map representation, which is designed to accommodate a wide variety of visual phenomena without assuming a particular imaging platform, thereby precluding the inference of scene geometry. Such a representation presents a requirement for a relatively dense sampling of observations of the environment in order to produce reliable models. Our goal is to develop an online policy for exploring an unknown environment which minimizes map distortion while maximizing coverage. We do not depend on costly post-hoc expectation maximization approaches to improve the output, but rather employ extended Kalman filter (EKF) methods to localize each observation once, and rely on the exploration policy to ensure that sufficient information is available to localize the successive observations. We present an experimental analysis of a variety of exploratory policies, in both simulated and real environments, and demonstrate that with an effective policy an accurate map can be constructed.

IROS Conference 2003 Conference Paper

Experiments in free-space triangulation using cooperative localization

• Ioannis M. Rekleitis
• Gregory Dudek
• Evangelos E. Milios

This paper presents a first detailed case study of collaborative exploration of a substantial environment. We use a pair of cooperating robots to test multi-robot environment mapping algorithms based on triangulation of free space. The robots observe one another using a robot tracking sensor based on laser range sensing (LIDAR). The environment mapping itself is accomplished using sonar sensing. The results of this mapping are compared to those obtained using scanning laser range sensing and the scan matching algorithm. We show that with appropriate outlier rejection policies, the sonar-based map obtained using collaborative localization can be as good or, in fact, better than that obtained using what is typically considered to be a superior sensing technology.

ICRA Conference 2003 Conference Paper

Path planning using learned constraints and preferences

• Gregory Dudek
• Saul Simhon

In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of probabilistic constraints to a "raw" input path. For example, a user can sketch an approximate path for a robot without considered issues such as bounded radius of curvature and our system would then elaborate it to include such a constraint. In our approach, the constraints that generate a path are learned by capturing the statistical properties of a set of training examples using supervised learning. Each training example consists of a pair of paths: an unconstrained (raw) path and an associated preferred path. Using a Hidden Markov Model in combination with multi-scale methods, we compute a probability distribution for successive path segments as a function of their context within the path and the raw path that guides them. This learned distribution is then used to synthesize a preferred path from an arbitrary input path by choosing some mixture of the training set biases that produce the maximum likelihood estimate. We present our method and applications for robot control and non-holonomic path planning.

ICRA Conference 2003 Conference Paper

Probabilistic cooperative localization and mapping in practice

• Ioannis M. Rekleitis
• Gregory Dudek
• Evangelos E. Milios

In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo Simulation technique (specifically, a Particle Filter) is employed in order to model and reduce the accumulated odometric error. Furthermore, we study the requirements to obtain an accurate yet timely pose estimate. A team of two robots is employed to explore an indoor environment in this paper, although several aspects of the approach have been extended to larger groups. The concept behind our exploration strategy has been presented previously and is based on having one robot carry a sensor that acts as a "robot tracker" to estimate the position of the other robot. By suitable use of the tracker as an appropriate motion-control mechanism we can sweep areas of free space between the stationary and the moving robot and generate an accurate graph-based description of the environment. This graph is used to guide the exploration process. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition (triangulation) of the environment. We present experimental results from indoor experiments in our laboratory and from more complex simulated experiments.

IROS Conference 2003 Conference Paper

Range synthesis for 3D environment modeling

• Luz Abril Torres-Méndez
• Gregory Dudek

This paper examines a novel method we have developed for computing range data in the context of mobile robotics. Our objective is to compute dense range maps of locations in the environment, but to do this using intensity images and very limited range data as input. We develop a statistical learning method for inferring and extrapolating range data from a combination of a single video intensity image and a limited amount of input range data. Our methodology is to compute the relationship between the observed range data and the variations in the intensity image, and use this to extrapolate new range values. These variations can be efficiently captured by the neighborhood system of a Markov random field (MRF) without making any strong assumptions about the kind of surfaces in the world. Experimental results show the feasibility of our method.

ICRA Conference 2003 Conference Paper

Robodaemon -a device independent, network-oriented, modular mobile robot controller

• Gregory Dudek
• Robert Sim

We discuss a software environment for multi-robot, multi-platform mobile robot control and simulation. Like others, we have observed that mobile robotics research is greatly facilitated by the availability of a suitable simulator for both vehicle kinematics as well as sensing, and have created an environment that permits this while allowing a large measure of device independence. By using a multiprocessor internet-based architecture, our platform permits multiple users to use a variety of programming interfaces (visual, script-based or various application programming interfaces (API's)) to rapidly prototype methods to control multiple heterogeneous robots both in simulation and in real-world settings. We present an overview of our architecture and discuss its future directions.

IROS Conference 2002 Conference Paper

Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy

• Ioannis M. Rekleitis
• Gregory Dudek
• Evangelos E. Milios

This paper examines the tradeoffs between different classes of sensing strategy and motion control strategy in the context of terrain mapping with multiple robots. We consider a larger group of robots that can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion. Here we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots.

IROS Conference 2001 Conference Paper

Collaborative exploration for the construction of visual maps

• Ioannis M. Rekleitis
• Robert Sim
• Gregory Dudek
• Evangelos E. Milios

We examine the problem of learning a visual map of the environment while maintaining an accurate pose estimate. Our approach is based on using two robots in a simple collaborative scheme. Without outside information, as a robot collects training images, its position estimate accumulates errors, thus corrupting its knowledge of the positions from which observations are taken. We address this problem by deploying a second robot to observe the first one as it explores, thereby establishing a virtual tether, and enabling an accurate estimate of the robot's position while it constructs the map. We refer to this process as cooperative localization. The images collected during this process are assembled into a representation that allows vision-based position estimation from a single image at a later date. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method in over 90 trials.

ICRA Conference 2000 Conference Paper

Multi-Robot Collaboration for Robust Exploration

• Ioannis M. Rekleitis
• Gregory Dudek
• Evangelos E. Milios

This paper presents a new sensing modality and stratagem for multirobot exploration. The approach is based on using pairs of robots that observe each other's behavior, acting in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. This allows the robots to obtain a map of higher accuracy than would be possible with robots acting independently by reducing inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reflectance properties. Two different algorithms, based on the size of the environment, are introduced with a complexity analysis, and experimental results in simulation and with real robots.

ICRA Conference 2000 Conference Paper

On-Line Construction of Iconic Maps

• Eric Bourque
• Gregory Dudek

This paper describes an approach to the automated creation of virtual realities (or virtual maps) of an a priori unknown environment by using a mobile robot. The method we propose is aimed at the creation of an image-based or iconic map, rather than a representation in terms of 2D or 3D spatial occupancy. A key aspect of this is having a mobile robot automatically select points and views of interest that can be used to exemplify the appearance of the environment. This paper develops the use of alpha-backtracking as a technique to efficiency select these points of estimated globally maximum interest.

ICRA Conference 2000 Conference Paper

Robust Place Recognition using Local Appearance Based Methods

• Gregory Dudek
• Deeptiman Jugessur

We present an approach to the automatic recognition of locations or landmarks using single camera images. Our approach is to learn visual features in the appearance domain that can be used to characterize an object or a location. These features are defined statistically and then are recognized using principal components in the frequency domain. We show that this technique can be used to recognize specific objects on varying backgrounds, as well as environmental features.

IROS Conference 2000 Conference Paper

The paparazzi problem

• Michael Jenkin
• Gregory Dudek

Multiple mobile robots, or robot collectives, have been proposed as solutions to various tasks in which distributed sensing and action are required. Here we consider applying a collective of robots to the paparazzi problem - the problem of providing sensor coverage of a target robot. We demonstrate how the computational task of the collective can be formulated as a global energy minimization task over the entire collective, and show how individual members of the collective can solve the task in a distributed fashion so that the entire collective meets its goal. This result is then extended to consider unbounded communication delays between members and complete failure of individual members of the collective.

ICRA Conference 1999 Conference Paper

Efficient Topological Exploration

• Ioannis M. Rekleitis
• Vida Dujmovic
• Gregory Dudek

We consider the robot exploration of a planar graph-like world. The robot's goal is to build a complete map of its environment. The environment is modeled as an arbitrary undirected planar graph which is initially unknown to the robot. The robot cannot distinguish vertices and edges that it has explored from the unexplored ones. The robot is assumed to be able to autonomously traverse graph edges, recognize when it has reached a vertex, and enumerate edges incident upon the current vertex. The robot cannot measure distances nor does it have a compass, but it is equipped with a single marker that it can leave at a vertex and sense if the marker is present at a newly visited vertex. The total number of edges traversed while constructing a map of a graph is used as a measure of performance. We present an efficient algorithm for learning an unknown, undirected planar graph by a robot equipped with one marker. Experimental results obtained by running a large collection of example worlds are presented.

ICRA Conference 1999 Conference Paper

Learning Visual Landmarks for Pose Estimation

• Robert Sim
• Gregory Dudek

We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features called image-domain landmarks. The landmark learning mechanism is designed to be applicable to a wide range of environments. Each landmark is detected as a focal extremum of a measure of uniqueness and represented by an appearance-based encoding. Localization is performed using a method that matches observed landmarks to learned prototypes and generates independent position estimates for each match. The independent estimates are then combined to obtain a final position estimate, with an associated uncertainty. Quantitative experimental evidence is presented that demonstrates that accurate pose estimates can be obtained, despite changes to the environment.

IROS Conference 1998 Conference Paper

A global topological map formed by local metric maps

• Saul Simhon
• Gregory Dudek

We describe a method of mapping large scale static environments using a hybrid topological-metric model. A global map is formed from a set of local maps organized in a topological structure. Each local map contains quantitative environment information using a local reference frame. They are denoted as islands of reliability because they provide accurate metric information of the environment. The mapping problem then becomes where to place the islands of reliability and to what extent should they cover the environment. This is accomplished by defining the placement criteria in terms of the task the islands of reliability portray.

IROS Conference 1998 Conference Paper

Mobile robot localization from learned landmarks

• Robert Sim
• Gregory Dudek

Presents an approach to vision-based mobile robot localization. In an attempt to capitalize on the benefits of both image and landmark-based methods, we describe a method that combines their strengths. Images are encoded as a set of visual features called landmarks. Potential landmarks are detected using an attention mechanism implemented as a measure of uniqueness. They are then selected and represented by an appearance-based encoding. Localization is performed using a landmark tracking and interpolation method which obtains an estimate accurate to a fraction of the environment sampling density. Experimental results are shown to confirm the feasibility and accuracy of the method.

ICRA Conference 1998 Conference Paper

Robotic Sightseeing: A Method for Automatically Creating Virtual Environments

• Eric Bourque
• Gregory Dudek
• Philipple Ciaravola

This paper describes the fully automatic creation of an environment's description using an image-based representation. This representation is a collection of cylindrical sample images combined into an "image-based virtual reality". The locations at which the environment will be sampled are chosen automatically using an operator inspired by models of human visual attention and saccadic motion. The image acquisition is performed by a mobile robot. The selection of vantage points is based on an analysis of the edge structure of sampled panoramic images. In order to trade off the optimality of the generated description of the navigation effort required in solving the online problem, a concept referred to as alpha-backtracking is introduced. The paper illustrates sample data acquired by the procedure.

ICRA Conference 1998 Conference Paper

Selecting Targets for Local Reference Frames

• Saul Simhon
• Gregory Dudek

Addresses the problem of seeking out parts of the environment that provide adequate features in order to perform robot localization. The objective is to choose good regions in which local metric maps can be established. A distinctiveness measure is defined as a measure of how well the environment allows the robot to accomplish a task, in our case the task being localization. The distinctiveness measure is evaluated as a function of both the localization strategy and the environment. Areas in the environment are considered to have high distinctiveness measures if they exhibit both sufficient spatial structure and good sensor feedback. The problem is treated as defining an evaluation criterion based on the usefulness of gathered information.

IROS Conference 1998 Conference Paper

Viewpoint selection-an autonomous robotic system for virtual environment creation

• Eric Bourque
• Gregory Dudek

Describes an integrated system for the automatic construction of image-based virtual realities to describe a real environment. A mobile robot autonomously navigates through the environment and uses a camera to make observations. At locations that are deemed sufficiently interesting, panoramic images are collected that are used to construct a multi-node VR movie. Images of the environment are classified in terms of two features related to human attention: edge element density and edge orientation. The system deems locations interesting if they are sufficiently different from the surrounding environment. The parameterization of the surrounding environment is computed either in a pre-computation pass, or online using a technique termed alpha-backtracking. The panoramic images that describe the environment are automatically joined together in a navigable movie that simulates motion in the real environment.

AAAI Conference 1997 Conference Paper

Autonomous Exploration: An Integrated Systems Approach

• Marc Bolduc
• Gregory Dudek

IJCAI Conference 1997 Conference Paper

Multi-Robot Exploration of an Unknown Environment, Efficiently Reducing the Odometry Error

• Ioannis M. Rekleitis
• Gregory Dudek
• Evangelos E. Milios

This paper deals with the intelligent exploration of an unknown environment by autonomous robots. In particular, we present an algorithm and associated analysis for collaborative exploration using two mobile robots. Our approach is based on robots with range sensors limited by distance. By appropriate behavioural strategies, we show that odometry (motion) errors that would normally present problems for mapping can be severely reduced. Our analysis includes polynomial complexity bounds and a discussion of possible heuristics.

IROS Conference 1997 Conference Paper

On the identification of sonar features

• Simon Lacroix
• Gregory Dudek

We are interested in inferring the sources of various types of sonar features typically observed by a mobile robot. After a brief discussion of terrestrial sonar sensing, we develop a set of operators that associates arc-shaped features extracted from sonar scans with real world primitives. Our classification scheme is probabilistic and is based on empirical data: the confidence of the association hypotheses produced by the operators is evaluated statistically. Some of our experimental results suggest that methods based on models of perfect sonar sensors may not be completely consistent with observed data. The management and merging of a collection of hypotheses concerning various sonar features allows the system to produce a coherent and mutually-compatible set of inferences for the entire observed environment.

ICRA Conference 1996 Conference Paper

Just-in-time sensing: efficiently combining sonar and laser range data for exploring unknown worlds

• Gregory Dudek
• Paul Freedman
• Ioannis M. Rekleitis

This paper describes an approach to combining range data from both a set of sonar sensors as well as from a directional laser range finder to efficiently take advantage of the characteristics of both types of devices when exploring and mapping unknown worlds. The authors call their approach "just in time sensing" because it uses the more accurate but constrained laser range sensor only as needed, based upon a preliminary interpretation of sonar data. In this respect, it resembles "just in time" inventory control which attempts to judiciously obtain materials for industrial manufacturing only when and as needed. Experiments with a mobile robot equipped with sonar and a laser rangefinder demonstrate that by judiciously using the more accurate but more complex laser rangefinder to deal with the well-known ambiguity which arises in sonar data, the authors are able to obtain a much better map of an interior space at little additional cost (in terms of time and computational expense).

ICRA Conference 1996 Conference Paper

Surface sensing and classification for efficient mobile robot navigation

• Nicholas Roy
• Gregory Dudek
• Paul Freedman

Mobile robot navigation and localization is frequently aided by, or even dependent upon, a good estimate of the rate of dead-reckoning error accumulation. Sensor data can be used for position estimation, but this often involves overheads in acquiring and processing the data. By sensing and then classifying the surface type, an estimate of the rate of error accumulation for dead-reckoning allows one to estimate accurately how often localization, including sensor data acquisition, must be performed. The authors describe experiments in which a boom-mounted microphone is tapped on different floor materials, much as a blind man might tap his cane. The acoustic signature arising from the contact is then used to classify the floor type by comparing a windowed power spectrum of the acoustic signature with one of a family of prototypical signatures generated statistically from the same material. The technique is low-cost, involves limited computational expense, and performs very well.

ICRA Conference 1996 Conference Paper

Vision-based robot localization without explicit object models

• Gregory Dudek
• Chi Zhang

We consider the problem of locating a robot in an initially-unfamiliar environment from visual input. The robot is not given a map of the environment, but it does have access to a collection of training examples, each of which specifies the video image observed when the robot is at a particular location and orientation. We address two variants of this problem: how to estimate translation of a moving robot assuming the orientation is known, and how to estimate translation and orientation for a mobile robot. Performing scene reconstruction to construct a metric map of the environment using only video images is difficult. We avoid this by using an approach in which the robot learns to convert a set of image measurements into a representation of its pose (position and orientation). This provides a metric estimate of the robot's location within a region covered by the statistical map we build. Localization can be performed online without a prior location estimate, The conversion from visual data to camera pose is implemented using a multilayer neural network that is trained using backpropagation. An aspect of the approach is the use of an inconsistency measure to eliminate incorrect data and estimate components of the pose vector. The experimental data reported in this paper suggests that the accuracy and flexibility of the technique is good, while the online computational cost is very low.

IROS Conference 1995 Conference Paper

Experiments in sensing and communication for robot convoy navigation

• Gregory Dudek
• Michael Jenkin
• Evangelos E. Milios
• David Wilkes

This paper deals with coordinating behaviour in a multi-autonomous robot system. When two or more autonomous robots must interact in order to accomplish some common goal, communication between the robots is essential. Different inter-robot communications strategies give rise to different overall system performance and reliability. After a brief consideration of some theoretical approaches to multiple robot collections, we present concrete implementations of different strategies for convoy-like behaviour. The convoy system is based around two RWI B12 mobile robots and uses only passive visual sensing for inter-robot communication. The issues related to different communication strategies are considered.

SODA Conference 1995 Conference Paper

Localizing a Robot with Minimum Travel

• Gregory Dudek
• Kathleen Romanik
• Sue Whitesides

IROS Conference 1995 Conference Paper

Space occupancy using multiple shadowimages

• Michael S. Langer
• Gregory Dudek
• Steven W. Zucker

Addresses the problem of estimating 3D space occupancy using video imagery in the context of mobile robotics. A stationary robot observes a cluttered scene from a single viewpoint, and a second robot illuminates the scene from a sequence of directions thus producing a sequence of grey-level images. Differences of successive images are used to compute a sequence of shadowimages. The problem is to compute free space and occupied space from these shadowimages. Solutions to this problem are known for the special case of terrain scenes. The authors generalize these solutions to non-terrain scenes by making two key observations. First, there is a subset constraint on the shadowimages of a non-terrain scene, which allows the visible surfaces of a non-terrain scene to be recovered by a terrain-based technique. Second, the remaining regions of the shadowimages provide a conservative estimate of the occupied space hidden by these visible surfaces.

ICRA Conference 1994 Conference Paper

Precise Positioning Using Model-Based Maps

• Paul MacKenzie
• Gregory Dudek

This paper addresses the coupled tasks of constructing a spatial representation of the environment with a mobile robot using noisy sensors (sonar) and using such a map to determine the robot's position. The map is not meant to represent the actual spatial structure of the environment so much as it is meant to represent the major structural components of what the robot "sees". This can, in turn, be used to construct a model of the physical objects in the environment. One problem with such an approach is that maintaining an absolute coordinate system for the map is difficult without periodically calibrating the robot's position. The authors demonstrate that in a suitable environment it is possible to use sonar data to correct position and orientation estimates on an ongoing basis. This is accomplished by incrementally constructing and updating a model-based description of the acquired data. Given coarse position estimates of the robot's location and orientation, these can be refined to high accuracy using the stored map and a set of sonar readings from a single position. This approach is then generalized to allow global position estimation, where position and orientation estimates may not be available. The authors consider the accuracy of the method based on a single sonar reading and illustrate its region of convergence using empirical data. >

IROS Conference 1993 Conference Paper

A taxonomy for swarm robots

• Gregory Dudek
• Michael Jenkin
• Evangelos E. Milios
• David Wilkes

In many cases several mobile robots (autonomous agents) can be used together to accomplish tasks that would be either more difficult or impossible for a robot acting alone. Many different models have been suggested for the makeup of such collections of robots. In this paper the authors present a taxonomy of the different ways in which such a collection of autonomous robotic agents can be structured. It is shown that certain swarms provide little or no advantage over having a single robot, while other swarms can obtain better than linear speedup over a single robot. There exist both trivial and non-trivial problems for which a swarm of robots can succeed where a single robot will fail. Swarms are more than just networks of independent processors - they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.