Author name cluster

Matthew R. Walter

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

30 papers

2 author rows

ICML Conference 2025 Conference Paper

PAC Learning with Improvements

Idan Attias
Avrim Blum
Keziah Naggita
Donya Saless
Dravyansh Sharma
Matthew R. Walter

One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes at least $1/\epsilon$ samples to learn to error $\epsilon$ (and more, if the classifier being learned is complex). However, suppose that data points are agents who have the ability to improve by a small amount if doing so will allow them to receive a (desired) positive classification. In that case, we may actually be able to achieve zero error by just being "close enough". For example, imagine a hiring test used to measure an agent’s skill at some job such that for some threshold $\theta$, agents who score above $\theta$ will be successful and those who score below $\theta$ will not (i. e. , learning a threshold on the line). Suppose also that by putting in effort, agents can improve their skill level by some small amount $r$. In that case, if we learn an approximation $\hat{\theta}$ of $\theta$ such that $\theta \leq \hat{\theta} \leq \theta + r$ and use it for hiring, we can actually achieve error zero, in the sense that (a) any agent classified as positive is truly qualified, and (b) any agent who truly is qualified can be classified as positive by putting in effort. Thus, the ability for agents to improve has the potential to allow for a goal one could not hope to achieve in standard models, namely zero error. In this paper, we explore this phenomenon more broadly, giving general results and examining under what conditions the ability of agents to improve can allow for a reduction in the sample complexity of learning, or alternatively, can make learning harder. We also examine both theoretically and empirically what kinds of improvement-aware algorithms can take into account agents who have the ability to improve to a limited extent when it is in their interest to do so.

IROS Conference 2025 Conference Paper

STACKGEN: Generating Stable Structures from Silhouettes via Diffusion

Luzhe Sun
Takuma Yoneda
Samuel Wheeler
Tianchong Jiang
Matthew R. Walter

Humans naturally obtain intuition about the interactions between and the stability of rigid objects by observing and interacting with the world. It is this intuition that governs the way in which we regularly configure objects in our environment, allowing us to build complex structures from simple, everyday objects. Robotic agents, on the other hand, traditionally require an explicit model of the world that includes the detailed geometry of each object and an analytical model of the environment dynamics, which are difficult to scale and preclude generalization. Instead, robots would benefit from an awareness of intuitive physics that enables them to similarly reason over the stable interaction of objects in their environment. Towards that goal, we propose STACKGEN—a diffusion model that generates diverse stable configurations of building blocks matching a target silhouette. To demonstrate the capability of the method, we evaluate it in a simulated environment and deploy it in the real setting using a robotic arm to assemble structures generated by the model. Our code is available at https://ripl.github.io/StackGen.

ICLR Conference 2024 Conference Paper

Blending Imitation and Reinforcement Learning for Robust Policy Improvement

Xuefeng Liu
Takuma Yoneda
Rick L. Stevens
Matthew R. Walter
Yuxin Chen 0001

While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample efficiency, yet it is often constrained by the quality of the oracles deployed. To address the demand for robust policy improvement in real-world scenarios, we introduce a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance. RPI draws on the strengths of IL, using oracle queries to facilitate exploration—an aspect that is notably challenging in sparse-reward RL—particularly during the early stages of learning. As learning unfolds, RPI gradually transitions to RL, effectively treating the learned policy as an improved oracle. This algorithm is capable of learning from and improving upon a diverse set of black-box oracles. Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner’s performance surpasses that of the oracles in a specific state. Empirical evaluations and theoretical analysis validate that RPI excels in comparison to existing state-of-the-art methodologies, demonstrating superior performance across various benchmark domains.

ICRA Conference 2024 Conference Paper

Statler: State-Maintaining Language Models for Embodied Reasoning

Takuma Yoneda
Jiading Fang
Peng Li
Huanyu Zhang
Tianchong Jiang
Shengjie Lin
Ben Picker
David Yunis

There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their abilities to reason about the histories of their actions and observations. In this paper, we explore a new dimension in which large language models may benefit robotics planning. In particular, we propose Statler, a framework in which large language models are prompted to maintain an estimate of the world state, which are often unobservable, and track its transition as new actions are taken. Our framework then conditions each action on the estimate of the current world state. Despite being conceptually simple, our Statler framework significantly outperforms strong competing methods (e. g. , Code-as-Policies) on several robot planning tasks. Additionally, it has the potential advantage of scaling up to more challenging long-horizon planning tasks. We release our code here.

NeurIPS Conference 2024 Conference Paper

Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning

David Yunis
Justin Jung
Falcon Z. Dai
Matthew R. Walter

Exploration in sparse-reward reinforcement learning (RL) is difficult due to the need for long, coordinated sequences of actions in order to achieve any reward. Skill learning, from demonstrations or interaction, is a promising approach to address this, but skill extraction and inference are expensive for current methods. We present a novel method to extract skills from demonstrations for use in sparse-reward RL, inspired by the popular Byte-Pair Encoding (BPE) algorithm in natural language processing. With these skills, we show strong performance in a variety of tasks, 1000$\times$ acceleration for skill-extraction and 100$\times$ acceleration for policy inference. Given the simplicity of our method, skills extracted from 1\% of the demonstrations in one task can be transferred to a new loosely related task. We also note that such a method yields a finite set of interpretable behaviors. Our code is available at https: //github. com/dyunis/subwords_as_skills.

PDF Details DOI

IROS Conference 2024 Conference Paper

Transcrib3D: 3D Referring Expression Resolution through Large Language Models

Jiading Fang
Xiangshan Tan
Shengjie Lin
Igor Vasiljevic
Vitor Guizilini
Hongyuan Mei
Rares Ambrus
Gregory Shakhnarovich

If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expressions is challenging—it requires the ability to both parse the 3D structure of the scene and correctly ground free-form language in the presence of distraction and clutter. We introduce Transcrib3D, an approach that brings together 3D detection methods and the emergent reasoning capabilities of large language models (LLMs). Transcrib3D uses text as the unifying medium, which allows us to sidestep the need to learn shared representations connecting multi-modal inputs, which would require massive amounts of annotated 3D data. As a demonstration of its effectiveness, Transcrib3D achieves state-of-the-art results on 3D reference resolution benchmarks, with a great leap in performance from previous multi-modality baselines. To improve upon zero-shot performance and facilitate local deployment on edge computers and robots, we propose self-correction for fine-tuning that trains smaller models, resulting in performance close to that of large models. We show that our method enables a real robot to perform pick-and-place tasks given queries that contain challenging referring expressions. Code will be available at https://ripl.github.io/Transcrib3D.

ICML Conference 2023 Conference Paper

Active Policy Improvement from Multiple Black-box Oracles

Xuefeng Liu
Takuma Yoneda
Chaoqi Wang
Matthew R. Walter
Yuxin Chen 0001

Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstrations to guide exploration. In real-world scenarios, one often has access to multiple suboptimal black-box experts, rather than a single optimal oracle. These experts do not universally outperform each other across all states, presenting a challenge in actively deciding which oracle to use and in which state. We introduce MAPS and MAPS-SE, a class of policy improvement algorithms that perform imitation learning from multiple suboptimal oracles. In particular, MAPS actively selects which of the oracles to imitate and improve their value function estimates, and MAPS-SE additionally leverages an active state exploration criterion to determine which states one should explore. We provide a comprehensive theoretical analysis and demonstrate that MAPS and MAPS-SE enjoy sample efficiency advantage over the state-of-the-art policy improvement algorithms. Empirical results show that MAPS-SE significantly accelerates policy optimization via state-wise imitation learning from multiple oracles across a broad spectrum of control tasks in the DeepMind Control Suite.

ICRA Conference 2022 Conference Paper

Self-Supervised Camera Self-Calibration from Video

Jiading Fang
Igor Vasiljevic
Vitor Guizilini
Rares Ambrus
Gregory Shakhnarovich
Adrien Gaidon
Matthew R. Walter

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by in-ferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. The project page: https://sites.google.com/ttic.edu/self-sup-self-calib

AAAI Conference 2021 Conference Paper

Loop Estimator for Discounted Values in Markov Reward Processes

Falcon Z. Dai
Matthew R. Walter

At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process. We propose a simple and efficient estimator called loop estimator that exploits the regenerative structure of Markov reward processes without explicitly estimating a full model. Our method enjoys a space complexity of O(1) when estimating the value of a single positive recurrent state s unlike TD with O(S ) or model-based methods with O S 2. Moreover, the regenerative structure enables us to show, without relying on the generative model approach, that the estimator has an instance-dependent convergence rate of e O √ τs/T over steps T on a single sample path, where τs is the maximal expected hitting time to state s. In preliminary numerical experiments, the loop estimator outperforms model-free methods, such as TD(k), and is competitive with the model-based estimator.

IROS Conference 2020 Conference Paper

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Jacopo Tani
Andrea F. Daniele
Gianmarco Bernasconi
Amaury Camus
Aleksandar Petrov
Anthony Courchesne
Bhairav Mehta
Rohit Suri

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challenges to benchmarking autonomy, such as the complexity of the software stacks, the variability of the hardware and the reliance on data-driven techniques, amongst others. In this paper, we describe a new concept for reproducible robotics research that integrates development and benchmarking, so that reproducibility is obtained "by design" from the beginning of the research/development processes. We first provide the overall conceptual objectives to achieve this goal and then a concrete instance that we have built: the DUCKIENet. One of the central components of this setup is the Duckietown Autolab, a remotely accessible standardized setup that is itself also relatively low-cost and reproducible. When evaluating agents, careful definition of interfaces allows users to choose among local versus remote evaluation using simulation, logs, or remote automated hardware setups. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs. †

ICML Conference 2020 Conference Paper

Multigrid Neural Memory

Tri Huynh
Michael Maire
Matthew R. Walter

We introduce a novel approach to endowing neural networks with emergent, long-term, large-scale memory. Distinct from strategies that connect neural networks to external memory banks via intricately crafted controllers and hand-designed attentional mechanisms, our memory is internal, distributed, co-located alongside computation, and implicitly addressed, while being drastically simpler than prior efforts. Architecting networks with multigrid structure and connectivity, while distributing memory cells alongside computation throughout this topology, we observe the emergence of coherent memory subsystems. Our hierarchical spatial organization, parameterized convolutionally, permits efficient instantiation of large-capacity memories, while multigrid topology provides short internal routing pathways, allowing convolutional networks to efficiently approximate the behavior of fully connected networks. Such networks have an implicit capacity for internal attention; augmented with memory, they learn to read and write specific memory locations in a dynamic data-dependent manner. We demonstrate these capabilities on exploration and mapping tasks, where our network is able to self-organize and retain long-term memory for trajectories of thousands of time steps. On tasks decoupled from any notion of spatial geometry: sorting, associative recall, and question answering, our design functions as a truly generic memory and yields excellent results.

ICRA Conference 2019 Conference Paper

Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions

Siddharth Patki
Andrea F. Daniele
Matthew R. Walter
Thomas M. Howard

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that improve the efficiency of language understanding. However, existing methods still attempt to reason over a representation of the environment that is flat and unnecessarily detailed, which limits scalability. An open problem is then to develop methods capable of producing the most compact environment model sufficient for accurate and efficient natural language understanding. We propose a model that leverages environment-related information encoded within instructions to identify the subset of observations and perceptual classifiers necessary to perceive a succinct, instruction-specific environment representation. The framework uses three probabilistic graphical models trained from a corpus of annotated instructions to infer salient scene semantics, perceptual classifiers, and grounded symbols. Experimental results on two robots operating in different environments demonstrate that by exploiting the content and the structure of the instructions, our method learns compact environment representations that significantly improve the efficiency of natural language symbol grounding.

ICRA Conference 2019 Conference Paper

Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning

Charles B. Schaff
David Yunis
Ayan Chakrabarti
Matthew R. Walter

The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment. In an increasing number of applications, data-driven and learning-based approaches, such as deep reinforcement learning, have proven effective at designing control policies. For most tasks, the only way to evaluate a physical design with respect to such control policies is empirical-i. e. , by picking a design and training a control policy for it. Since training these policies is time-consuming, it is computationally infeasible to train separate policies for all possible designs as a means to identify the best one. In this work, we address this limitation by introducing a method that jointly optimizes over the physical design and control network. Our approach maintains a distribution over designs and uses reinforcement learning to optimize a control policy to maximize expected reward over the design distribution. We give the controller access to design parameters to allow it to tailor its policy to each design in the distribution. Throughout training, we shift the distribution towards higher-performing designs, eventually converging to a design and control policy that are jointly optimal. We evaluate our approach in the context of legged locomotion, and demonstrate that it discovers novel designs and walking gaits, outperforming baselines across different settings.

IROS Conference 2017 Conference Paper

Jointly optimizing placement and inference for beacon-based localization

Charles B. Schaff
David Yunis
Ayan Chakrabarti
Matthew R. Walter

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.

ICRA Conference 2017 Conference Paper

Satellite image-based localization via learned embeddings

Dong-Ki Kim
Matthew R. Walter

We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance variations between the images through a neural multi-view model that learns location-discriminative embeddings in which ground-level images are matched with their corresponding satellite view of the scene. We use this learned function as an observation model in a filtering framework to maintain a distribution over the vehicle's pose. We evaluate our method on different benchmark datasets and demonstrate its ability localize ground-level images in environments novel relative to training, despite the challenges of significant viewpoint and appearance variations.

IROS Conference 2015 Conference Paper

Information-theoretic dialog to improve spatial-semantic representations

Sachithra Hemachandra
Matthew R. Walter

We propose an algorithm that enables robots to improve their spatial-semantic representation of an environment by engaging users in dialog during a guided tour. The algorithm selects the best information gathering actions in the form of targeted questions that reduce the ambiguity over the grounding of user-provided natural language descriptions (e. g. , “The kitchen is down the hallway”). These questions include those that query the robot's local surround (e. g. , “Are we in front of the kitchen? ”) as well as areas distant from the robot (e. g. , “Is the lounge near the conference room? ”). Our algorithm treats dialog as an optimization problem that seeks to balance the information-theoretic value of candidate questions with a measure of cost associated with dialog. In this manner, the algorithm determines the best questions to ask based upon the expected entropy reduction, while accounting for the burden on the user. We evaluate entropy reduction for a joint distribution over a hybrid metric, topological, and semantic representation of the environment learned from user-provided descriptions and the robot's sensor data during the guided tour. We demonstrate that, by asking deliberate questions of the user, the method significantly improves the accuracy of the learned map.

ICRA Conference 2015 Conference Paper

Learning models for following natural language directions in unknown environments

Sachithra Hemachandra
Felix Duvallet
Thomas M. Howard
Nicholas Roy
Anthony Stentz
Matthew R. Walter

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.

IROS Conference 2015 Conference Paper

On the performance of hierarchical distributed correspondence graphs for efficient symbol grounding of robot instructions

Istvan Chung
Oron Propp
Matthew R. Walter
Thomas M. Howard

Natural language interfaces are powerful tools that enables humans and robots to convey information without the need for extensive training or complex graphical interfaces. Statistical techniques that employ probabilistic graphical models have proven effective at interpreting symbols that represent commands and observations for robot direction-following and object manipulation. A limitation of these approaches is their inefficiency in dealing with larger and more complex symbolic representations. Herein, we present a model for language understanding that uses parse trees and environment models both to learn the structure of probabilistic graphical models and to perform inference over this learned structure for symbol grounding. This model, called the Hierarchical Distributed Correspondence Graph (HDCG), exploits information about symbols that are expressed in the corpus to construct minimalist graphical models that are more efficient to search. In a series of comparative experiments, we demonstrate a significant improvement in efficiency without loss in accuracy over contemporary approaches for human-robot interaction.

ICRA Conference 2014 Conference Paper

A summary of team MIT's approach to the virtual robotics challenge

Russ Tedrake
Maurice F. Fallon
Sisir Karumanchi
Scott Kuindersma
Matthew E. Antone
Toby Schneider
Thomas M. Howard
Matthew R. Walter

The attached video describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency's (DARPA) Virtual Robotics Challenge (VRC), held in June 2013. The VRC was the first competition in the DARPA Robotics Challenge (DRC), a program that aims to “develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments” [1]. The VRC required teams to guide a model of Boston Dynamics' humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation.

ICRA Conference 2014 Conference Paper

Learning spatial-semantic representations from natural language descriptions and scene classifications

Sachithra Hemachandra
Matthew R. Walter
Stefanie Tellex
Seth J. Teller

We describe a semantic mapping algorithm that learns human-centric environment models by interpreting natural language utterances. Underlying the approach is a coupled metric, topological, and semantic representation of the environment that enables the method to fuse information from natural language descriptions with low-level metric and appearance data. We extend earlier work with a novel formulation that incorporates spatial layout into a topological representation of the environment. We also describe a factor graph formulation of the semantic properties that encodes human-centric concepts such as type and colloquial name for each mapped region. The algorithm infers these properties by combining the user's natural language descriptions with image- and laser-based scene classification. We also propose a mechanism to more effectively ground natural language descriptions of distant regions using semantic cues from other modalities. We describe how the algorithm employs this learned semantic information to propose valid topological hypotheses, leading to more accurate topological and metric maps. We demonstrate that integrating language with other sensor data increases the accuracy of the achieved spatial-semantic representation of the environment.

ICRA Conference 2011 Conference Paper

Anytime Motion Planning using the RRT

Sertac Karaman
Matthew R. Walter
Alejandro Perez
Emilio Frazzoli
Seth J. Teller

The Rapidly-exploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly produces candidate feasible solutions, it tends to converge to a solution that is far from optimal. Practical applications favor "anytime" algorithms that quickly identify an initial feasible plan, then, given more computation time available during plan execution, improve the plan toward an optimal solution. This paper describes an anytime algorithm based on the RRT* which (like the RRT) finds an initial feasible solution quickly, but (unlike the RRT) almost surely converges to an optimal solution. We present two key extensions to the RRT% committed trajectories and branch-and-bound tree adaptation, that together enable the algorithm to make more efficient use of computation time online, resulting in an anytime algorithm for real-time implementation. We evaluate the method using a series of Monte Carlo runs in a high-fidelity simulation environment, and compare the operation of the RRT and RRT* methods. We also demonstrate experimental results for an outdoor wheeled robotic vehicle.

IROS Conference 2011 Conference Paper

Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms

Alejandro Perez
Sertac Karaman
Alexander C. Shkolnik
Emilio Frazzoli
Seth J. Teller
Matthew R. Walter

A desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over high-dimensional configuration spaces and to perform computationally expensive collision checking procedures. Consequently, existing planners take steps to achieve desired solution times at the cost of low quality solutions. This paper presents a planning algorithm that overcomes these difficulties by augmenting the asymptotically-optimal RRT* with a sparse sampling procedure. With the addition of a collision checking procedure that leverages memoization, this approach has the benefit that it quickly identifies low-cost feasible trajectories and takes advantage of subsequent computation time to refine the solution towards an optimal one. We evaluate the algorithm through a series of Monte Carlo simulations of seven, twelve, and fourteen degree of freedom manipulation planning problems in a realistic simulation environment. The results indicate that the proposed approach provides significant improvements in the quality of both the initial solution and the final path, while incurring almost no computational overhead compared to the RRT algorithm. We conclude with a demonstration of our algorithm for single-arm and dual-arm planning on Willow Garage's PR2 robot.

ICRA Conference 2010 Conference Paper

A voice-commandable robotic forklift working alongside humans in minimally-prepared outdoor environments

Seth J. Teller
Matthew R. Walter
Matthew E. Antone
Andrew Correa
Randall Davis
Luke Fletcher
Emilio Frazzoli
James R. Glass

One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in existing human workplaces in a way that their presence is accepted by the human occupants. We describe the development of a multi-ton robotic forklift intended to operate alongside human personnel, handling palletized materials within existing, busy, semi-structured outdoor storage facilities.

IROS Conference 2010 Conference Paper

Closed-loop pallet manipulation in unstructured environments

Matthew R. Walter
Sertac Karaman
Emilio Frazzoli
Seth J. Teller

This paper addresses the problem of autonomous manipulation of a priori unknown palletized cargo with a robotic lift truck (forklift). Specifically, we describe coupled perception and control algorithms that enable the vehicle to engage and place loaded pallets relative to locations on the ground or truck beds. Having little prior knowledge of the objects with which the vehicle is to interact, we present an estimation framework that utilizes a series of classifiers to infer the objects' structure and pose from individual LIDAR scans. The classifiers share a low-level shape estimation algorithm that uses linear programming to robustly segment input data into sets of weak candidate features. We present and analyze the performance of the segmentation method, and subsequently describe its role in our estimation algorithm. We then evaluate the performance of a motion controller that, given an estimate of a pallet's pose, is employed to safely engage each pallet. We conclude with a validation of our algorithms for a set of real-world pallet and truck interactions.

ICRA Conference 2010 Conference Paper

Ground robot navigation using uncalibrated cameras

Olivier Koch
Matthew R. Walter
Albert S. Huang
Seth J. Teller

Precise calibration of camera intrinsic and extrinsic parameters, while often useful, is difficult to obtain during field operation and presents scaling issues for multi-robot systems. We demonstrate a vision-based approach to navigation that does not depend on traditional camera calibration, and present an algorithm for guiding a robot through a previously traversed environment using a set of uncalibrated cameras mounted on the robot. On the first excursion through an environment, the system builds a topological representation of the robot's exploration path, encoded as a place graph. On subsequent navigation missions, the method localizes the robot within the graph and provides robust guidance to a specified destination. We combine this method with reactive collision avoidance to obtain a system able to navigate the robot safely and reliably through the environment. We validate our approach with ground-truth experiments and demonstrate the method on a small ground rover navigating through several dynamic environments.

ICRA Conference 2009 Conference Paper

Consistent cooperative localization

Alexander Bahr
Matthew R. Walter
John J. Leonard

In cooperative navigation, teams of mobile robots obtain range and/or angle measurements to each other and dead-reckoning information to help each other navigate more accurately. One typical approach is moving baseline navigation, in which multiple Autonomous Underwater Vehicles (AUVs) exchange range measurements using acoustic modems to perform mobile trilateration. While the sharing of information between vehicles can be highly beneficial, exchanging measurements and state estimates can also be dangerous because of the risk of measurements being used by a vehicle more than once; such data re-use leads to inconsistent (overconfident) estimates, making data association and outlier rejection more difficult and divergence more likely. In this paper, we present a technique for the consistent cooperative localization of multiple AUVs performing mobile trilateration. Each AUV establishes a bank of filters, performing careful bookkeeping to track the origins of measurements and prevent the use any of the measurements more than once. The multiple estimates are combined in a consistent manner, yielding conservative covariance estimates. The technique is illustrated using simulation results. The new method is compared side-by-side with a naive approach that does not keep track of the origins of measurements, illustrating that the new method keeps conservative covariance bounds whereas state estimates obtained with the naive approach become overconfident and diverge.

ICRA Conference 2009 Conference Paper

Reachability-guided sampling for planning under differential constraints

Alexander C. Shkolnik
Matthew R. Walter
Russ Tedrake

Rapidly-exploring random trees (RRTs) are widely used to solve large planning problems where the scope prohibits the feasibility of deterministic solvers, but the efficiency of these algorithms can be severely compromised in the presence of certain kinodynamics constraints. Obstacle fields with tunnels, or tubes are notoriously difficult, as are systems with differential constraints, because the tree grows inefficiently at the boundaries. Here we present a new sampling strategy for the RRT algorithm, based on an estimated feasibility set, which affords a dramatic improvement in performance in these severely constrained systems. We demonstrate the algorithm with a detailed look at the expansion of an RRT in a swing up task, and on path planning for a nonholonomic car.

ICRA Conference 2009 Conference Paper

Simultaneous local and global state estimation for robotic navigation

David C. Moore
Albert S. Huang
Matthew R. Walter
Edwin Olson
Luke Fletcher
John J. Leonard
Seth J. Teller

Recent applications of robotics often demand two types of spatial awareness: 1) A fine-grained description of the robot's immediate surroundings for obstacle avoidance and planning, and 2) Knowledge of the robot's position in a large-scale global coordinate frame such as that provided by GPS. Although managing information at both of these scales is often essential to the robot's purpose, each scale has different requirements in terms of state representation and handling of uncertainty. In such a scenario, it can be tempting to pick either a body-centric coordinate frame or a globally fixed coordinate frame for all state representation. Although both choices have advantages, we show that neither is ideal for a system that must handle both global and local data. This paper describes an alternative design: a third coordinate frame that stays fixed to the local environment over short time-scales, but can vary with respect to the global frame. Careful management of uncertainty in this local coordinate frame makes it well-suited for simultaneously representing both locally and globally derived data, greatly simplifying system design and improving robustness. We describe the implementation of this coordinate frame and its properties when measuring uncertainty, and show the results of applying this approach to our 2007 DARPA Urban Challenge vehicle.

ICRA Conference 2008 Conference Paper

SLAM for ship hull inspection using exactly sparse extended information filters

Matthew R. Walter
Franz S. Hover
John J. Leonard

Many important missions for autonomous underwater vehicles (AUVs), such as undersea inspection of ship hulls, require integrated navigation, control, and motion planning in complex, 3D environments. This paper describes a SLAM implementation using forward-looking sonar (FLS) data from a highly maneuverable, hovering AUV performing a ship hull inspection mission. The exactly sparse extended information filter (ESEIF) algorithm is applied to perform SLAM based upon features manually selected within FLS images. The results demonstrate the ability to effectively map a ship hull in a challenging marine environment. This provides a foundation for future work in which real-time SLAM will be integrated with motion planning and control to achieve autonomous coverage of a complete ship hull.

IROS Conference 2005 Conference Paper

Sparse extended information filters: insights into sparsification

Ryan M. Eustice
Matthew R. Walter
John J. Leonard

Recently, there have been a number of variant simultaneous localization and mapping (SLAM) algorithms that have made substantial progress towards large-area scalability by parameterizing the SLAM posterior within the information (canonical/inverse covariance) form. Of these, probably the most well known and popular approach is the sparse extended information filter (SEIF) by Thrun et al. While SEIFs have been successfully implemented with a variety of challenging real world datasets and have led to new insights into scalable SLAM, open research questions remain regarding the approximate sparsification procedure and its effect on map error consistency. In this paper, we examine the constant time SEIF sparsification procedure in depth and offer new insight into issues of consistency. In particular, we show that exaggerated map inconsistency occurs within the global reference frame where estimation is performed, but that empirical testing shows that relative local map relationships are preserved. We then present a slightly modified version of their sparsification procedure, which is shown to preserve sparsity while also generating both local and global map estimates comparable to those obtained by the nonsparsified SLAM filter. While this modified approximation is no longer constant time, it does serve as a theoretical benchmark against which to compare SEIFs constant time results. We demonstrate our findings by benchmark comparison of the modified and original SEIF sparsification rule using simulation in the linear Gaussian SLAM case and real world experiments for a nonlinear dataset.