Author name cluster

Daniel D. Lee

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54 papers

2 author rows

IROS Conference 2023 Conference Paper

AmbiSense: Acoustic Field Based Blindspot-Free Proximity Detection and Bearing Estimation

Siddharth Rupavatharam
Xiaoran Fan
Caleb Escobedo
Daewon Lee
Larry D. Jackel
Richard E. Howard
Colin Prepscius
Daniel D. Lee

In this paper, we present AmbiSense, an acoustic field based sensing system that performs proximity detection and bearing estimation for safer physical human-robot interactions. A single low cost piezoelectric transducer is used to setup this novel acoustic sensing modality to create a blindspot-free sound field engulfing a robot arm. Two detection algorithms leveraging spectral information from reflected audio waves of objects entering the acoustic field are proposed to infer object presence and bearing. We also present a new receiver structure which improves signal to noise ratio (SNR). AmbiSense is paired with a collision avoidance inverse kinematic solver for real world deployment on a Kinova Gen3 robot. Validation is performed using ten test objects generating 2000 proximity and bearing estimation events in real world settings, we show that AmbiSense detects proximity with 93. 8% sensitivity and 96. 6 % specificity. It estimates bearing and maps it to three zones on a robot link with 100% sensitivity and specificity, while using fewer sensors than state of the art methods for similar coverage.

AAAI Conference 2022 Conference Paper

Cooperative Multi-Agent Fairness and Equivariant Policies

Niko A. Grupen
Bart Selman
Daniel D. Lee

We study fairness through the lens of cooperative multi-agent learning. Our work is motivated by empirical evidence that naive maximization of team reward yields unfair outcomes for individual team members. To address fairness in multiagent contexts, we introduce team fairness, a group-based fairness measure for multi-agent learning. We then prove that it is possible to enforce team fairness during policy optimization by transforming the team’s joint policy into an equivariant map. We refer to our multi-agent learning strategy as Fairness through Equivariance (Fair-E) and demonstrate its effectiveness empirically. We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that it reaches higher levels of utility than Fair-E and fairer outcomes than non-equivariant policies. Finally, we present novel findings regarding the fairnessutility trade-off in multi-agent settings; showing that the magnitude of the trade-off is dependent on agent skill.

IROS Conference 2022 Conference Paper

Learning from Demonstration using a Curvature Regularized Variational Auto-Encoder (CurvVAE)

Travers Rhodes
Tapomayukh Bhattacharjee
Daniel D. Lee

Learning intricate manipulation skills from human demonstrations requires good sample efficiency. We introduce a novel learning algorithm, the Curvature-regularized Variational Auto-Encoder (CurvVAE), to achieve this goal. The CurvVAE is able to model the natural variations in human-demonstrated trajectory data without overfitting. It does so by regularizing the curvature of the learned manifold. To showcase our algorithm, our robot learns an interpretable model of the variation in how humans acquire soft, slippery banana slices with a fork. We evaluate our learned trajectories on a physical robot system, resulting in banana slice acquisition performance better than current state-of-the-art.

AAMAS Conference 2022 Conference Paper

Multi-Agent Curricula and Emergent Implicit Signaling

Niko A. Grupen
Daniel D. Lee
Bart Selman

Emergent communication has made strides towards learning communication from scratch, but has focused primarily on protocols that resemble human language. In nature, multi-agent cooperation gives rise to a wide range of communication that varies in structure and complexity. In this work, we recognize the full spectrum of communication that exists in nature and propose studying lower-level communication. Speci�cally, we study emergent implicit signaling in the context of decentralized multi-agent learning in di�cult, sparse reward environments. However, learning to coordinate in such environments is challenging. We propose a curriculum-driven strategy that combines: (i) velocity-based environment shaping, tailored to the skill level of the multi-agent team; and (ii) a behavioral curriculum that helps agents learn successful single-agent behaviors as a precursor to learning multi-agent behaviors. Pursuitevasion experiments show that our approach learns e�ective coordination, signi�cantly outperforming sophisticated analytical and learned policies. Our method completes the pursuit-evasion task even when pursuers move at half of the evader’s speed, whereas the highest-performing baseline fails at 80% of the evader’s speed. Moreover, we examine the use of implicit signals in coordination through position-based social in�uence. We show that pursuers trained with our strategy exchange more than twice as much information (in bits) than baseline methods, indicating that our method has learned, and relies heavily on, the exchange of implicit signals.

IROS Conference 2022 Conference Paper

Simultaneous Object Reconstruction and Grasp Prediction using a Camera-centric Object Shell Representation

Nikhil Chavan Dafle
Sergiy Popovych
Shubham Agrawal
Daniel D. Lee
Volkan Isler

Being able to grasp objects is a fundamental component of most robotic manipulation systems. In this paper, we present a new approach to simultaneously reconstruct a mesh and a dense grasp quality map of an object from a depth image. At the core of our approach is a novel camera-centric object representation called the “object shell” which is composed of an observed “entry image” and a predicted “exit image”. We present an image-to-image residual ConvNet architecture in which the object shell and a grasp-quality map are predicted as separate output channels. The main advantage of the shell representation and the corresponding neural network architecture, ShellGrasp-Net, is that the input-output pixel correspondences in the shell representation are explicitly represented in the architecture. We show that this coupling yields superior generalization capabilities for object reconstruction and accurate grasp quality estimation implicitly considering the object geometry. Our approach yields an efficient dense grasp quality map and an object geometry estimate in a single forward pass. Both of these outputs can be used in a wide range of robotic manipulation applications. With rigorous experimental validation, both in simulation and on a real setup, we show that our shell-based method can be used to generate precise grasps and the associated grasp quality with over 90% accuracy. Diverse grasps computed on shell reconstructions allow the robot to select and execute grasps in cluttered scenes with more than 93% success rate.

IROS Conference 2021 Conference Paper

AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection

Xiaoran Fan
Riley Simmons-Edler
Daewon Lee
Larry D. Jackel
Richard E. Howard
Daniel D. Lee

Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95. 3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

ICRA Conference 2021 Conference Paper

Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

Jinwook Huh
Volkan Isler
Daniel D. Lee

This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efficiently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function. We compare c2g-HOF with traditional planning algorithms for various robots and planning scenarios. The experimental results indicate that planning with c2g-HOF is significantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. Furthermore, despite being trained from sparsely sampled trajectories in configuration space, c2g-HOF generalizes to generate smoother, and often lower cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0. 13 seconds for the entire trajectory.

ICRA Conference 2021 Conference Paper

Deep Reinforcement Learning for Active Target Tracking

Heejin Jeong
Seyed Hamed Hassani
Manfred Morari
Daniel D. Lee
George J. Pappas

We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked with acquiring information about targets of interests using its on-board sensors. The classical challenges in this problem are system model dependence and the difficulty of computing information-theoretic cost functions for a long planning horizon. RL provides solutions for these challenges as the length of its effective planning horizon does not affect the computational complexity, and it drops the strong dependency of an algorithm on system models. In particular, we introduce Active Tracking Target Network (ATTN), a unified deep RL policy that is capable of solving major sub-tasks of active target tracking – in-sight tracking, navigation, and exploration. The policy shows robust behavior for tracking agile and anomalous targets with a partially known target model. Additionally, the same policy is able to navigate in obstacle environments to reach distant targets as well as explore the environment when targets are positioned in unexpected locations.

ICRA Conference 2021 Conference Paper

Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors

Anthony Bisulco
Fernando Cladera
Volkan Isler
Daniel D. Lee

This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high-speed motion. As a representative scenario we consider a robot at rest, reacting to a small, fast approaching object at speeds higher than 15 m/s. Since conventional image sensors at typical frame rates observe such an object for only a few frames, estimating the underlying motion presents a considerable challenge for standard computer vision systems and algorithms. We present a method motivated by how animals such as insects solve this problem with their relatively simple vision systems. Our solution takes the event stream from a DVS and first encodes the temporal events with a set of causal exponential filters across multiple time scales. We couple these filters with a Convolutional Neural Network (CNN) to efficiently extract relevant spatiotemporal features. The combined network learns to output both the expected time to collision of the object, as well as the predicted collision point on a discretized polar grid. These critical estimates are computed with minimal delay by the network in order to react appropriately to the incoming object. We highlight our system’s results with a toy dart moving at 23. 4 m/s with a 24. 73° error in θ, 18. 4 mm average discretized radius prediction error, and 25. 03% median time to collision prediction error.

AAAI Conference 2021 Conference Paper

Geodesic-HOF: 3D Reconstruction Without Cutting Corners

Ziyun Wang
Eric A. Mitchell
Volkan Isler
Daniel D. Lee

Single-view 3D object reconstruction is a challenging fundamental problem in machine perception, largely due to the morphological diversity of objects in the natural world. In particular, high curvature regions are not always represented accurately by methods trained with common set-based loss functions such as Chamfer Distance, resulting in reconstructions short-circuiting the surface or “cutting corners. ” To address this issue, we propose an approach to 3D reconstruction that embeds points on the surface of an object into a higherdimensional space that captures both the original 3D surface as well as geodesic distances between points on the surface of the object. The precise specification of these additional “lifted” coordinates ultimately yields useful surface information without requiring excessive additional computation during either training or testing, in comparison with existing approaches. Our experiments show that taking advantage of these learned lifted coordinates yields better performance for estimating surface normals and generating surfaces than using point cloud reconstructions alone. Further, we find that this learned geodesic embedding space provides useful information for applications such as unsupervised object decomposition.

IROS Conference 2021 Conference Paper

Learning Continuous Cost-to-Go Functions for Non-holonomic Systems

Jinwook Huh
Daniel D. Lee
Volkan Isler

This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with uniform sampling. To address this challenge, we present an adaptive sampling method which makes use of sampling based planners along with local, closed-form solutions to generate training samples. The cost-to-go function over a specific workspace is represented as a neural network whose weights are generated by a second, higher order network. The networks are trained in an end-to-end fashion. In our previous work, this architecture was shown to successfully learn to generate the cost-to-go functions of holonomic systems using uniform sampling. In this work, we show that uniform sampling fails for non-holonomic systems. However, with the proposed adaptive sampling methodology, our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles. Experiments show that our method is two orders of magnitude faster compared to traditional approaches in cluttered environments.

ICRA Conference 2021 Conference Paper

Occupancy Map Inpainting for Online Robot Navigation

Minghan Wei
Daewon Lee
Volkan Isler
Daniel D. Lee

In this work, we focus on mobile robot navigation in indoor environments where occlusions and field-of-view limitations hinder onboard sensing capabilities. We show that the footprint of a camera mounted on a robot can be drastically improved using learning-based approaches. Specifically, we consider the task of building an occupancy map for autonomous navigation of a robot equipped with a depth camera. In our approach, a local occupancy map is first computed using measurements from the camera directly. Afterwards, an inpainting network adds further information, the occupancy probabilities of unseen grid cells, to the map. A novel aspect of our approach is that rather than direct supervision from ground truth, we combine the information from a second camera with a better field-of-view for supervision. The training focuses on predicting extensions of the sensed data. To test the effectiveness of our approach, we use a robot setup with a single camera placed at 0. 5m above the ground. We compare the navigation performance using raw maps from only this camera’s input (baseline) versus using inpainted maps augmented with our network. Our method outperforms the baseline approach even in completely new environments not included in the training set and can yield 21% shorter paths than the baseline approach. A real-time implementation of our method on a mobile robot is also tested in home and office environments.

ICRA Conference 2021 Conference Paper

Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction

Daniel Yang
Tarik Tosun
Ben Eisner
Volkan Isler
Daniel D. Lee

We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network. Our system generates 6-DOF grasps from a single RGB-D image of the target object, which is provided as input to both networks. By using the geometric reconstruction to refine the candidate grasp produced by the grasp proposal network, our system is able to accurately grasp both known and unknown objects, even when the grasp location on the object is not visible in the input image. This paper presents the network architectures, training procedures, and grasp refinement method that comprise our system. Experiments demonstrate the efficacy of our system at grasping both known and unknown objects (91% success rate in a physical robot environment, 84% success rate in a simulated environment). We additionally perform ablation studies that show the benefits of combining a learned grasp proposal with geometric reconstruction for grasping, and also show that our system outperforms several baselines in a grasping task.

IROS Conference 2020 Conference Paper

Acoustic Collision Detection and Localization for Robot Manipulators

Xiaoran Fan
Daewon Lee
Yuan Chen 0006
Colin Prepscius
Volkan Isler
Larry D. Jackel
H. Sebastian Seung
Daniel D. Lee

Collision detection is critical for safe robot operation in the presence of humans. Acoustic information originating from collisions between robots and objects provides opportunities for fast collision detection and localization; however, audio information from microphones on robot manipulators needs to be robustly differentiated from motors and external noise sources. In this paper, we present Panotti, the first system to efficiently detect and localize on-robot collisions using low-cost microphones. We present a novel algorithm that can localize the source of a collision with centimeter level accuracy and is also able to reject false detections using a robust spectral filtering scheme. Our method is scalable, easy to deploy, and enables safe and efficient control for robot manipulator applications. We implement and demonstrate a prototype that consists of 8 miniature microphones on a 7 degree of freedom (DOF) manipulator to validate our design. Extensive experiments show that Panotti realizes near perfect on-robot true positive collision detection rate with almost zero false detections even in high noise environments. In terms of accuracy, it achieves an average localization error of less than 3. 8 cm under various experimental settings.

ICRA Conference 2020 Conference Paper

Higher Order Function Networks for View Planning and Multi-View Reconstruction

Selim Engin
Eric Mitchell
Daewon Lee
Volkan Isler
Daniel D. Lee

We consider the problem of planning views for a robot to acquire images of an object for visual inspection and reconstruction. In contrast to offline methods which require a 3D model of the object as input or online methods which rely on only local measurements, our method uses a neural network which encodes shape information for a large number of objects. We build on recent deep learning methods capable of generating a complete 3D reconstruction of an object from a single image. Specifically, in this work, we extend a recent method which uses Higher Order Functions (HOF) to represent the shape of the object. We present a new generalization of this method to incorporate multiple images as input and establish a connection between visibility and reconstruction quality. This relationship forms the foundation of our view planning method where we compute viewpoints to visually cover the output of the multiview HOF network with as few images as possible. Experiments indicate that our method provides a good compromise between online and offline methods: Similar to online methods, our method does not require the true object model as input. In terms of number of views, it is much more efficient. In most cases, its performance is comparable to the optimal offline case even on object classes the network has not been trained on.

ICLR Conference 2020 Conference Paper

Higher-Order Function Networks for Learning Composable 3D Object Representations

Eric Mitchell
Selim Engin
Volkan Isler
Daniel D. Lee

We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network. This mapping network can be used to reconstruct an object by applying its encoded transformation to points randomly sampled from a simple geometric space, such as the unit sphere. We study the effectiveness of our method through various experiments on subsets of the ShapeNet dataset. We find that the proposed approach can reconstruct encoded objects with accuracy equal to or exceeding state-of-the-art methods with orders of magnitude fewer parameters. Our smallest mapping network has only about 7000 parameters and shows reconstruction quality on par with state-of-the-art object decoder architectures with millions of parameters. Further experiments on feature mixing through the composition of learned functions show that the encoding captures a meaningful subspace of objects.

IJCAI Conference 2019 Conference Paper

Assumed Density Filtering Q-learning

Heejin Jeong
Clark Zhang
George J. Pappas
Daniel D. Lee

While off-policy temporal difference (TD) methods have widely been used in reinforcement learning due to their efficiency and simple implementation, their Bayesian counterparts have not been utilized as frequently. One reason is that the non-linear max operation in the Bellman optimality equation makes it difficult to define conjugate distributions over the value functions. In this paper, we introduce a novel Bayesian approach to off-policy TD methods, called as ADFQ, which updates beliefs on state-action values, Q, through an online Bayesian inference method known as Assumed Density Filtering. We formulate an efficient closed-form solution for the value update by approximately estimating analytic parameters of the posterior of the Q-beliefs. Uncertainty measures in the beliefs not only are used in exploration but also provide a natural regularization for the value update considering all next available actions. ADFQ converges to Q-learning as the uncertainty measures of the Q-beliefs decrease and improves common drawbacks of other Bayesian RL algorithms such as computational complexity. We extend ADFQ with a neural network. Our empirical results demonstrate that ADFQ outperforms comparable algorithms on various Atari 2600 games, with drastic improvements in highly stochastic domains or domains with a large action space.

IROS Conference 2019 Conference Paper

Learning Q-network for Active Information Acquisition

Heejin Jeong
Brent Schlotfeldt
Seyed Hamed Hassani
Manfred Morari
Daniel D. Lee
George J. Pappas

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors. The classic challenges in the information acquisition problem are the dependence of a planning algorithm on known models and the difficulty of computing information-theoretic cost functions over arbitrary distributions. In contrast, the proposed framework of reinforcement learning does not require any knowledge on models and alleviates the problems during an extended training stage. It results in policies that are efficient to execute online and applicable for real-time control of robotic systems. Furthermore, the state-of-the-art planning methods are typically restricted to short horizons, which may become problematic with local minima. Reinforcement learning naturally handles the issue of planning horizon in information problems as it maximizes a discounted sum of rewards over a long finite or infinite time horizon. We discuss the potential benefits of the proposed framework and compare the performance of the novel algorithm to an existing information acquisition method for multi-target tracking scenarios.

ICRA Conference 2019 Conference Paper

Online Continuous Mapping using Gaussian Process Implicit Surfaces

Bhoram Lee
Clark Zhang
Zonghao Huang
Daniel D. Lee

The representation of the environment strongly affects how robots can move and interact with it. This paper presents an online approach for continuous mapping using Gaussian Process Implicit Surfaces (GPISs). Compared with grid-based methods, GPIS better utilizes sparse measurements to represent the world seamlessly. It provides direct access to the signed-distance function (SDF) and its derivatives which are invaluable for other robotic tasks and it incorporates uncertainty in the sensor measurements. Our approach incrementally and efficiently updates GPIS by employing a regressor on observations and a spatial tree structure. The effectiveness of the suggested approach is demonstrated using simulations and real world 2D/3D data.

IROS Conference 2019 Conference Paper

Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner

Tarik Tosun
Eric Mitchell
Ben Eisner
Jinwook Huh
Bhoram Lee
Daewon Lee
Volkan Isler
H. Sebastian Seung

We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate “expert” training trajectories from a small amount of human-labeled data. In contrast to the traditional sense-plan-act cycle, we propose a deep learning architecture and training regimen called PtPNet that can estimate effective end-effector trajectories for manipulation directly from a single RGB-D image of an object. Additionally, we present a data collection and augmentation pipeline that enables the automatic generation of large numbers (millions) of training image and trajectory examples with almost no human labeling effort. We demonstrate our approach in a non-prehensile tool-based manipulation task, specifically picking up shoes with a hook. In hardware experiments, PtPNet generates motion plans (open-loop trajectories) that reliably (89% success over 189 trials) pick up four very different shoes from a range of positions and orientations, and reliably picks up a shoe it has never seen before. Compared with a traditional sense-plan-act paradigm, our system has the advantages of operating on sparse information (single RGB-D frame), producing high-quality trajectories much faster than the expert planner (300ms versus several seconds), and generalizing effectively to previously unseen shoes. Video available at https://youtu.be/voIkyiBtwn4.

IROS Conference 2018 Conference Paper

Artificial Invariant Subspace for Humanoid Robot Balancing in Locomotion

Xiang Deng
Daniel D. Lee

Legged robots that make use of compliant actuators have demonstrated greater robustness of locomotion than their rigid counterparts. Stiffness and damping are key parameters that characterize the adaptation to perturbations. In this work, by drawing inspirations from controllable compliance and damping in existing soft and bio-inspired legged robots, we propose an approach to design a nonlinear controller for the balancing of humanoid robots with rigid bodies. Existing literature has proposed simplified dynamical models of biped robots in order to predict the timing and placement of swing foot for walking without falling. We further employ the properties of invariance to perturbations in damped harmonic oscillators and formulate continuous feedback control in combination with predictive foot stepping in order to achieve continuous adaptive recoveries of the nominal walking cycle from unexpected physical disturbances. Our method allows asymptotic convergence of the rigid body dynamics to a subspace with the desired energy level. We demonstrate the robustness of the proposed algorithm base on extensive push recovery experiments on a NAO robot on flat terrains.

ICRA Conference 2018 Conference Paper

Constrained Sampling-Based Planning for Grasping and Manipulation

Jinwook Huh
Bhoram Lee
Daniel D. Lee

This paper presents a novel constrained, sampling-based motion planning method for grasp and transport tasks with a redundant robotic manipulator. We utilize a planning margin for grasping with constraints that allow the best grasp configuration and approach direction to be determined automatically. For manipulators with many degrees of freedom, our method efficiently chooses the optimal grasp pose when there are many redundant solutions. The method also introduces a parameterized intermediate pose that is optimized to determine the approach direction, increasing robustness under sensor uncertainty and execution errors. Our method also considers transporting the grasped object to the desired target position using a Rapidly-exploring Random Tree (RRT) algorithm that incorporates soft constraints via appropriate cost penalties. We demonstrate the effectiveness and efficiency of our algorithms on a number of simulated and experimental applications. Our experimental results show a marked improvement in computational efficiency in comparison to previously studied approaches.

IROS Conference 2018 Conference Paper

Learning Implicit Sampling Distributions for Motion Planning

Clark Zhang
Jinwook Huh
Daniel D. Lee

Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficient to not uniformly explore the state space, especially when there is prior information about its structure. Previous methods have attempted to modify the sampling distribution using hand selected heuristics that can work well for specific environments but not universally. In this paper, a policy-search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. It utilizes information from past searches in similar environments to generate better distributions in novel environments, thus reducing overall computational cost. Our method can be incorporated with a variety of sampling-based planners to improve performance. Our approach is validated on a number of tasks, including a 7DOF robot arm, showing marked improvement in number of collision checks as well as number of nodes expanded compared with baseline methods.

IROS Conference 2018 Conference Paper

Minimal Construct: Efficient Shortest Path Finding for Mobile Robots in Polygonal Maps

Marcell Missura
Daniel D. Lee
Maren Bennewitz

With the advent of polygonal maps finding their way into the navigational software of mobile robots, the Visibility Graph can be used to search for the shortest collision-free path. The nature of the Visibility Graph-based shortest path algorithms is such that first the entire graph is computed in a relatively time-consuming manner. Then, the graph can be searched efficiently any number of times for varying start and target state combinations with the A* or the Dijkstra algorithm. However, real-world environments are typically too dynamic for a map to remain valid for a long time. With the goal of obtaining the shortest path quickly in an ever changing environment, we introduce a rapid path finding algorithm-Minimal Construct-that discovers only a necessary portion of the Visibility Graph around the obstacles that actually get in the way. Collision tests are computed only for lines that seem heuristically promising. This way, shortest paths can be found much faster than with a state-of-the-art Visibility Graph algorithm and as our experiments show, even grid-based A* searches are outperformed in most cases with the added benefit of smoother and shorter paths.

ICRA Conference 2017 Conference Paper

Adaptive motion planning with high-dimensional mixture models

Jinwook Huh
Bhoram Lee
Daniel D. Lee

This paper presents a novel adaptive approach to fast sampling-based motion planning by learning models of collision and collision-free regions in configuration spaces in an online manner. The proposed approach incrementally learns Gaussian Mixture Models (GMMs) for collision detection in high dimensional configuration spaces. In practical applications for robotic manipulation, the representation of collision and collision-free regions in configuration space can change due to relative motion between the robot base and workspace. We show how to rapidly adapt to such changes by using inverse kinematics to transform the parameters of the Gaussian mixture model to new configurations. The transformed model is initially used as a prior and then continually updated and refined as the RRT planning algorithm proceeds in real-time. This approach is extremely computationally efficient, and our proposed method is compared with traditional sampling-based planning methods on a number of experimental robot arm planning scenarios.

IROS Conference 2017 Conference Paper

Artificial invariant subspace with potential functions for humanoid robot balancing

Xiang Deng
Fei Miao
Daniel D. Lee

Existing trajectory planning based locomotion algorithms lack the analytic tools to fully comprehend energy based movements that would allow for full stability and mobility. Such drawbacks make humanoid robots' locomotion sensitive to external disturbances and compromise robots' agility in unstructured environment. In this work, we specifically focus on the push recovery problem for humanoid robots. We propose an approach to design a nonlinear controller that is robust to external disturbances. It allows the state of the rigid body dynamics to asymptotically converge to the subspace that meet the criteria of balancing, based on the properties of artificial invariant subspace and potential functions. Our algorithm is completely adaptive in real time without requiring trajectory planning in advance. We demonstrate the robustness of the proposed algorithm base on extensive push recovery experiments on the DARWIN-OP robot platform on flat terrains.

IROS Conference 2017 Conference Paper

Self-supervised online learning of appearance for 3D tracking

Bhoram Lee
Daniel D. Lee

This paper presents a self-supervised online learning approach for 3D object tracking that requires no pretraining of appearance. Our method focuses on selecting the most relevant parts of the RGBD input by continuously updating appearance classifiers in conjunction with the spatial occupancy of the target. Fine-grained regions selected via the learned bottom-up saliency, together with spatial cues of the 3D shape model, are used to identify and localize the target via shape registration. The subsequent 3-D pose estimate along with positive and negative labels from the registration are used for online learning appearance. The proposed method outperforms competing model-based tracking algorithms on public datasets as well as on a new motion scene dataset that we have collected.

IROS Conference 2017 Conference Paper

The synchronized holonomic model: A framework for efficient generation of motion

Marcell Missura
Daniel D. Lee
Oskar von Stryk
Maren Bennewitz

We present a simple and efficient mathematical framework suitable for generating motion in the context of a variety of robotic motion tasks ranging from low-level motor control up to high-level locomotion planning. Our concept is based on a one-dimensional second-order model that allows analytic computation of its inverse dynamics while respecting physical constraints. This makes it a particularly useful tool for tasks that are expressed only as a start and goal state, such as animation key frames or way points in path planning. By means of time synchronization, the model extends easily to an arbitrary number of dimensions in a way that the target is reached in all dimensions at the same time. The framework excels in terms of execution time, which lies in the microsecond range even for high-dimensional trajectory generation tasks. We demonstrate our method in two different settings - full-body trajectory generation and path planning - and show its benefits in comparison with current state-of-the-art algorithms.

IROS Conference 2016 Conference Paper

Efficient learning of stand-up motion for humanoid robots with bilateral symmetry

Heejin Jeong
Daniel D. Lee

Standing up after falling is an essential ability for humanoid robots in order to resume their tasks without help from humans. Although many humanoid robots, especially small-size humanoid robots, have their own stand-up motions, there has not been a generalized method to automatically learn flexible stand-up motions for humanoid robots which can be applied to various fallen positions. In this research, we propose a method for learning stand-up motions for humanoid robots using Q-learning making use of their bilateral symmetry. We implemented this method on DarwIn-OP humanoid robots and learned an optimal policy in simulation. We compared the resulting stand-up motion with manually designed stand-up motions and with stand-up motions learned without considering bilateral symmetry. Both in simulation and on the real robot, the new stand-up motion was successful in most trials while other motions took longer or were not as robust.

IROS Conference 2016 Conference Paper

Heel and toe lifting walk controller for resource constrained humanoid robots

Seung-Joon Yi
Daniel D. Lee

Common design principles for low cost humanoid robots include a low center of mass height and a large support area for increased static stability. However, such principles limit the bipedal mobility of the robot due to the kinematic constraints involved. In this paper, we present an efficient locomotion controller that utilizes automatically calculated heel and toe lift motions to overcome the kinematic constraints. This helps with uneven terrain traversal by providing additional support, and also enables a dynamic heel-strike toe-off gait with a large stride length. We demonstrate the controller in physically realistic simulations, and on the THOR-RD full-sized humanoid robot and DARwIn-OP miniature humanoid robot.

ICRA Conference 2016 Conference Paper

Learning anisotropic ICP (LA-ICP) for robust and efficient 3D registration

Bhoram Lee
Daniel D. Lee

This paper presents an online learning approach to 3D object registration that vastly improves the performance of Iterative Closest Point (ICP) methods. Our approach achieves better robustness and stable convergence by learning generalized distance functions directly from a stream of object depth data. The proposed algorithm, Learning Anisotropic ICP (LA-ICP), parameterizes the point uncertainty of the underlying object surface as an anisotropic Gaussian, and estimates the covariance parameters of the likelihood function for ICP from data. Our learning scheme does not require manual tuning and the parameters of the algorithm are continually updated from observed data. Experiments on various RGB-D object datasets demonstrate the effectiveness of our approach in terms of convergence and pose accuracy as well as robustness to initial conditions.

ICRA Conference 2016 Conference Paper

Learning high-dimensional Mixture Models for fast collision detection in Rapidly-Exploring Random Trees

Jinwook Huh
Daniel D. Lee

This paper presents a new approach for fast collision detection in high dimensional configuration spaces for Rapidly-exploring Random Trees (RRT) motion planning. The proposed method is based upon Gaussian Mixture Models (GMM) that are learned using an incremental Expectation Maximization clustering algorithm trained online using exemplars provided by a slow, conventional kinematic-based collision detection routine. The number of collision checks needed can be drastically reduced using a biased random sampling from the learned GMM distribution, and the learned models are continually refined and improved as the RRT planning algorithm proceeds. Our proposed method is demonstrated on several example applications and experimental results show marked improvement in computational efficiency over previous approaches.

ICRA Conference 2016 Conference Paper

Low dimensional human preference tracking for motion optimization

Stephen G. McGill
Seung-Joon Yi
Daniel D. Lee

Motion planning for high degree of freedom (DOF) robots is not an easy task, and often requires optimization in a high dimensional space. Still, a generic motion planner using a single cost function for optimization may not be optimal over a number of different tasks with various task specific constraints. In this paper, we present a motion planning system that utilizes both easy to communicate human preferences and dimensionality reduction to handle these issues. Joint trajectories with human preference costs are projected into the null space of the task space, which helps make the resulting optimization simpler and more reliable. In addition, we apply the dimensionality reduction for the optimization, which significantly lowers the computational load. The suggested controller has been successfully used in the DARPA Robotics Challenge (DRC) Finals to handle a number of manipulation tasks.

IROS Conference 2016 Conference Paper

Online learning of visibility and appearance for object pose estimation

Bhoram Lee
Daniel D. Lee

This paper presents an online self-supervised approach to improve the quality and relevance of input point cloud to a 3D registration algorithm. The suggested method considers the visibility of the model points and learns discriminative appearance of the object under gradual changes. It selectively reduces the amount of information to process by excluding non-visible points of the model and removing outliers from data stream, which results in better alignment between the input data and the model. Thus, by providing a good initial pose, it speeds up the iterative procedure of EM-like optimization for pose estimation (i. e. , ICP) to achieve better efficiency and robustness. We compiled a new object dataset of RGBD images under camera motion with ground truth poses of the camera and the objects. We have performed experiments on this dataset and obtained promising results.

IROS Conference 2015 Conference Paper

Dynamic and probabilistic estimation of manipulable obstacles for indoor navigation

Christopher Clingerman
Peter J. Wei
Daniel D. Lee

In this paper we derive and implement an algorithm for an indoor mobile robotics platform to estimate the manipulability of initially unknown obstacles while navigating through its environment to a pre-specified goal. The environment is represented by an evidence grid, where each cell contains a gamma-distributed cost as well as visual feature data in the form of a color histogram. While navigating, the robot associates visual features of objects occupying a given cell with manipulability cost estimates of that cell, learning whether an object or obstacle can be moved or not in the robot's attempt to reach the goal. We derive and utilize a lower confidence bound (LCB) estimate for the cost of each cell in order to incorporate an exploration (versus pure exploitation) element to the robot's search for the lowest-cost path. Combining the LCB cost estimates with the dynamic replanning search algorithm D*-Lite, we can quickly compute optimal navigation paths regardless of the numerous changes occurring in the robot's environmental belief state. We explain the probabilistic representation of cost in the evidence grid and provide simulation and real-world results for our algorithm in a navigation scenario with static and movable objects.

ICRA Conference 2015 Conference Paper

Online self-supervised monocular visual odometry for ground vehicles

Bhoram Lee
Kostas Daniilidis
Daniel D. Lee

This paper presents an online self-supervised approach to monocular visual odometry and ground classification applied to ground vehicles. We solve the motion and structure problem based on a constrained kinematic model. The true scale of the monocular scene is recovered by estimating the ground surface. We consider a general parametric ground surface model and use the Random Sample Consensus (RANSAC) algorithm for robust fitting of the parameters. The estimated ground surface provides training samples to learn a probabilistic appearance-based ground classifier in an online and self-supervised manner. The appearance-based classifier is then used to bias the RANSAC sampling to generate better hypotheses for parameter estimation of the ground surface model. Thus, without relying on any prior information, we combine geometric estimates with appearance-based classification to achieve an online self-learning scheme from monocular vision. Experimental results demonstrate that online learning improves the computational efficiency and accuracy compared to standard sampling in RANSAC. Evaluations on the KITTI benchmark dataset demonstrate the stability and accuracy of our overall methods in comparison to previous approaches.

ICRA Conference 2014 Conference Paper

Estimating manipulability of unknown obstacles for navigation in indoor environments

Christopher Clingerman
Daniel D. Lee

The challenging task of navigating in cluttered environments has been studied extensively with indoor autonomous mobile robots. However, few approaches attempt to estimate real-valued costs for manipulating said obstacles with no prior knowledge of the environment. Our approach not only estimates these costs but also models the uncertainty inherent in making such estimates. We present an algorithm that, with no prior knowledge of the environment, allows a mobile robot to determine which obstacles are movable and which are not while navigating a cluttered environment. The algorithm also applies this knowledge of manipulability to obstacles encountered in the future that are similar in appearance to ones previously seen. Using our approach, a mobile robot can act intelligently about uncertain information as well as successfully navigate initially unknown indoor environments without relying on human-provided information.

IROS Conference 2014 Conference Paper

Modular low-cost humanoid platform for disaster response

Seung-Joon Yi
Stephen G. McGill
Larry Vadakedathu
Qin He
Inyong Ha
Michael Rouleau
Dennis W. Hong
Daniel D. Lee

Developing a reliable humanoid robot that operates in uncharted real-world environments is a huge challenge for both hardware and software. Commensurate with the technology hurdles, the amount of time and money required can also be prohibitive barriers. This paper describes Team THOR's approach to overcoming such barriers for the 2013 DARPA Robotics Challenge (DRC) Trials. We focused on forming modular components - in both hardware and software - to allow for efficient and cost effective parallel development. The robotic hardware consists of standardized and general purpose actuators and structural components. These allowed us to successfully build the robot from scratch in a very short development period, modify configurations easily and perform quick field repair. Our modular software framework consists of a hybrid locomotion controller, a hierarchical arm controller and a platform-independent operator interface. These modules helped us to keep up with hardware changes easily and to have multiple control options to suit various situations. We validated our approach at the DRC Trials where we fared very well against robots many times more expensive.

ICRA Conference 2013 Conference Paper

Online learning of low dimensional strategies for high-level push recovery in bipedal humanoid robots

Seung-Joon Yi
Byoung-Tak Zhang
Dennis W. Hong
Daniel D. Lee

Bipedal humanoid robots will fall under unforeseen perturbations without active stabilization. Humans use dynamic full body behaviors in response to perturbations, and recent bipedal robot controllers for balancing are based upon human biomechanical responses. However these controllers rely on simplified physical models and accurate state information, making them less effective on physical robots in uncertain environments. In our previous work, we have proposed a hierarchical control architecture that learns from repeated trials to switch between low-level biomechanically-motivated strategies in response to perturbations. However in practice, it is hard to learn a complex strategy from limited number of trials available with physical robots. In this work, we focus on the very problem of efficiently learning the high-level push recovery strategy, using simulated models of the robot with different levels of abstraction, and finally the physical robot. From the state trajectory information generated using different models and a physical robot, we find a common low dimensional strategy for high level push recovery, which can be effectively learned in an online fashion from a small number of experimental trials on a physical robot. This learning approach is evaluated in physics-based simulations as well as on a small humanoid robot. Our results demonstrate how well this method stabilizes the robot during walking and whole body manipulation tasks.

IROS Conference 2012 Conference Paper

Active stabilization of a humanoid robot for impact motions with unknown reaction forces

Seung-Joon Yi
Byoung-Tak Zhang
Dennis W. Hong
Daniel D. Lee

During heavy work, humans utilize whole body motions in order to generate large forces. In extreme cases, exaggerated weight shifts are used to impart large impact forces. There have been approaches to design stable whole body impact motions based on precise dynamic models of the robot and the target object, but they have practical limitations as the uncertainty in the ensuing reaction forces can lead to instability. In the current work, we describe a motion controller for a humanoid robot that generates impacts at an end effector while keeping the robot body balanced before and after the impact. Instead of relying on the accuracy of the impact dynamics model, we use a simplified model of the robot and biomechanically motivated push recovery controllers to reactively stabilize the robot against unknown perturbations from the impact. We demonstrate our approach in physically realistic simulations, as well as experimentally on a small humanoid robot platform.

ICRA Conference 2011 Conference Paper

Efficient dynamic programming for high-dimensional, optimal motion planning by spectral learning of approximate value function symmetries

Paul Vernaza
Daniel D. Lee

We demonstrate how to find high-quality motion plans for high-dimensional holonomic systems efficiently using dynamic programming in a learned subspace of vastly reduced dimension. Our approach (SLASHDP) learns the low dimensional cost structure of an optimal control problem via an efficient spectral method. This structure results in a symmetric value function that serves as a an efficiently-computable surrogate for the true value function. High-quality feedback motion plans can then be obtained from the symmetric value function. Experimental results show that SLASHDP yields higher-quality plans than can be obtained by post-processing plans generated by a sampling-based motion planner, and with less computational effort for very high-dimensional problems. We demonstrate high-quality dynamic programming plans for an arm planning problem of up to 144 dimensions without using any domain-specific knowledge aside from that learned automatically by SLASHDP. Positive results are also shown for a high-dimensional deformable robot planning problem.

IROS Conference 2011 Conference Paper

Learning Dimensional Descent planning for a highly-articulated robot arm

Paul Vernaza
Daniel D. Lee

We present an method for generating high-quality plans for a robot arm with many degrees of freedom based on Learning Dimensional Descent (LDD), a recently-developed algorithm for planning in high-dimensional spaces based on machine learning and optimization techniques. Unlike other approaches used to solve this problem, our method optimizes a well-defined objective and can be shown to generate optimal plans, in theory and practice, for a well-defined class of problems—those that possess low-dimensional cost structure. For the common case where such structure is only approximately present, LDD constitutes a powerful iterative optimization technique that makes non-homotopic path adjustments in each iteration, while still providing a guarantee of convergence to a local minimum of the objective. Experiments with a 7-DOF robot arm show that the method is able to find solutions in cluttered environments that are of a much higher quality than can be obtained with sampling-based planners and smoothing.

ICRA Conference 2011 Conference Paper

Learning full body push recovery control for small humanoid robots

Seung-Joon Yi
Byoung-Tak Zhang
Dennis W. Hong
Daniel D. Lee

Dynamic bipedal walking is susceptible to external disturbances and surface irregularities, requiring robust feedback control to remain stable. In this work, we present a practical hierarchical push recovery strategy that can be readily implemented on a wide range of humanoid robots. Our method consists of low level controllers that perform simple, biomechanically motivated push recovery actions and a high level controller that combines the low level controllers according to proprioceptive and inertial sensory signals and the current robot state. Reinforcement learning is used to optimize the parameters of the controllers in order to maximize the stability of the robot over a broad range of external disturbances. The controllers are learned on a physical simulation and implemented on the Darwin-HP humanoid robot platform, and the resulting experiments demonstrate effective full body push recovery behaviors during dynamic walking.

IROS Conference 2011 Conference Paper

Practical bipedal walking control on uneven terrain using surface learning and push recovery

Seung-Joon Yi
Byoung-Tak Zhang
Dennis W. Hong
Daniel D. Lee

Bipedal walking in human environments is made difficult by the unevenness of the terrain and by external disturbances. Most approaches to bipedal walking in such environments either rely upon a precise model of the surface or special hardware designed for uneven terrain. In this paper, we present an alternative approach to stabilize the walking of an inexpensive, commercially-available, position-controlled humanoid robot in difficult environments. We use electrically compliant swing foot dynamics and onboard sensors to estimate the inclination of the local surface, and use a online learning algorithm to learn an adaptive surface model. Perturbations due to external disturbances or model errors are rejected by a hierarchical push recovery controller, which modulates three biomechanically motivated push recovery controllers according to the current estimated state. We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial DARwIn-OP small humanoid robot. Experimental results show that this combined approach enables the robot to walk over unknown, uneven surfaces without falling down.

ICRA Conference 2010 Conference Paper

Learning and planning high-dimensional physical trajectories via structured Lagrangians

Paul Vernaza
Daniel D. Lee
Seung-Joon Yi

We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to synthesize new trajectories. Our approach models physical trajectories as least-time trajectories realized by free particles moving along the geodesics of a curved manifold, reminiscent of the way light rays obey Fermat's principle of least time. Finding these trajectories, unfortunately, requires finding a minimum-cost path in a high-dimensional space, which is generally a computationally intractable problem. In this work we show that this high-dimensional planning problem can often be solved nearly optimally in practice via deterministic search, as long as we can find a certain low-dimensional structure in the Lagrangian that describes our observed trajectories. This low-dimensional structure additionally makes it feasible to learn an estimate of a Lagrangian that is consistent with the observed trajectories, thus allowing us to present a complete approach for learning from and predicting high-dimensional physical motion sequences. We finally show experimental results applying our method to human motion and robotic walking gaits. In doing so, we furthermore demonstrate efficient path planning in a 990-dimensional space.

ICRA Conference 2010 Conference Paper

Scalable real-time object recognition and segmentation via cascaded, discriminative Markov random fields

Paul Vernaza
Daniel D. Lee

We present a method for real-time simultaneous object recognition and segmentation based on cascaded discriminative Markov random fields. A Markov random field models coupling between the labels of adjacent image regions. The MRF affinities are learned as linear functions of image features in a structured max-margin framework that admits a solution via convex optimization. In contrast to other known MRF/CRF-based approaches, our method classifies in real-time and has computational complexity that scales only logarithmically in the number of object classes. We accomplish this by applying a cascade of binary MRF-classifiers in a way similar to error-correcting output coding for general multiclass learning problems. Inference in this model is exact and can be performed very efficiently using graph cuts. Experimental results are shown that demonstrate a marked improvement in classification accuracy over purely local methods.

ICRA Conference 2009 Conference Paper

Search-based planning for a legged robot over rough terrain

Paul Vernaza
Maxim Likhachev
Subhrajit Bhattacharya
Sachin Chitta
Aleksandr Kushleyev
Daniel D. Lee

We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains.

ICML Conference 2008 Conference Paper

Grassmann discriminant analysis: a unifying view on subspace-based learning

Jihun Ham
Daniel D. Lee

In this paper we propose a discriminant learning framework for problems in which data consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we can make learning algorithms adapt naturally to the problems with linear invariant structures. We propose a unifying view on the subspace-based learning method by formulating the problems on the Grassmann manifold, which is the set of fixed-dimensional linear subspaces of a Euclidean space. Previous methods on the problem typically adopt an inconsistent strategy: feature extraction is performed in the Euclidean space while non-Euclidean distances are used. In our approach, we treat each sub-space as a point in the Grassmann space, and perform feature extraction and classification in the same space. We show feasibility of the approach by using the Grassmann kernel functions such as the Projection kernel and the Binet-Cauchy kernel. Experiments with real image databases show that the proposed method performs well compared with state-of-the-art algorithms.

ICRA Conference 2008 Conference Paper

Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields

Paul Vernaza
Ben Taskar
Daniel D. Lee

The authors present a novel approach to the task of autonomous terrain classification based on structured prediction. We consider the problem of learning a classifier that will accurately segment an image into “obstacle” and “ground” patches based on supervised input. Previous approaches to this problem have focused mostly on local appearance; typically, a classifier is trained and evaluated on a pixel-bypixel basis, making an implicit assumption of independence in local pixel neighborhoods. We relax this assumption by modeling correlations between pixels in the submodular MRF framework. We show how both the learning and inference tasks can be simply and efficiently implemented-exact inference via an efficient max flow computation; and learning, via an averaged-subgradient method. Unlike most comparable MRFbased approaches, our method is suitable for implementation on a robot in real-time. Experimental results are shown that demonstrate a marked increase in classification accuracy over standard methods in addition to real-time performance.

ICRA Conference 2007 Conference Paper

Proprioceptive localilzatilon for a quadrupedal robot on known terrain

Sachin Chitta
Paul Vemaza
Roman Geykhman
Daniel D. Lee

We present a novel method for the localization of a legged robot on known terrain using only proprioceptive sensors such as joint encoders and an inertial measurement unit. In contrast to other proprioceptive pose estimation techniques, this method allows for global localization (i. e. , localization with large initial uncertainty) without the use of exteroceptive sensors. This is made possible by establishing a measurement model based on the feasibility of putative poses on known terrain given observed joint angles and attitude measurements. Results are shown that demonstrate that the method performs better than dead-reckoning, and is also able to perform global localization from large initial uncertainty

ICRA Conference 2006 Conference Paper

Rao-Blackwellized Particle Filtering for 6-DOF Estimation of Attitude and Position via GPS and Inertial Sensors

Paul Vernaza
Daniel D. Lee

The authors present an innovative method for the efficient joint estimation of attitude and position in six degrees of freedom via sensors such as GPS, inertial measurement units, and odometry. Traditional methods for attitude estimation via Kalman filtering are beset by many conceptual problems relating to the representation of orientations in linear spaces, leading to difficulties in implementation and the interpretation of uncertainty estimates, among other issues. These problems are compounded when it is necessary to jointly estimate position and attitude. We demonstrate how Rao-Blackwellized particle filtering provides a framework for approaching this estimation problem that is both conceptually appealing and practical. Results are shown that demonstrate the filter's robustness to sensor outages and its ability to perform well even in situations with noisy sensors and high initial uncertainty in all state dimensions; these situations are precisely those in which traditional Kalman filtering approaches are most likely to experience problems

IROS Conference 2005 Conference Paper

Cooperative relative robot localization with audible acoustic sensing

Yuanqing Lin
Paul Vernaza
Jihun Ham
Daniel D. Lee

We describe a method for estimating the relative poses of a team of mobile robots using only acoustic sensing. The relative distances and bearing angles of the robots are estimated using the time of arrival of audible sound signals on stereo microphones. The robots emit specially designed sound waveforms that simultaneously enable robot identification and time of arrival estimation. These acoustic observations are then combined with odometry to update a belief state describing the positions and heading angles of all the robots. To efficiently resolve the ambiguity in the heading angle of the observing robot as well as the back-front ambiguity of the observed robot, we employ a Rao-Blackwellised particle filter (RBPF) where the distribution over heading angles is represented by a discrete set of particles, and the uncertainty in the translational positions conditioned on each of these particles is described by a Gaussian. This approach combines the representational accuracy of conventional particle filters with the efficiency of Kalman filter updates in modeling the pose distribution over a number of robots. We demonstrate how the RBPF can quickly resolve uncertainties in the binaural acoustic measurements and yield a globally consistent pose estimate. Simulations as well as an experimental implementation on robots with generic sound hardware illustrate the accuracy and the convergence of the resulting pose estimates.

IROS Conference 2005 Conference Paper

Learning nonlinear appearance manifolds for robot localization

Jihun Ham
Yuanqing Lin
Daniel D. Lee

We propose a nonlinear method for learning the low-dimensional pose of a robot from high-dimensional panoramic images. The panoramic images are assumed to lie on a nonlinear low-dimensional appearance manifold that is embedded in a high-dimensional image space. We demonstrate that the local geometry of a point and its nearest neighbors on this manifold can be used to project the point onto a low-dimensional coordinate space. Using this embedding, the unknown camera position can be estimated from a novel panoramic image. We show how the image-based position measurements can be integrated with odometry information in a Bayesian framework to yield an online estimate of a robot's position. Results from simulated data show that the proposed method outperforms other appearance-based models based upon principal components analysis and kernel density estimation.

ICML Conference 2004 Conference Paper

A kernel view of the dimensionality reduction of manifolds

Jihun Ham
Daniel D. Lee
Sebastian Mika
Bernhard Schölkopf

We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.