Showing papers by "Willow Garage published in 2010"

Fast 3D recognition and pose using the Viewpoint Feature Histogram

Abstract: We present the Viewpoint Feature Histogram (VFH), a descriptor for 3D point cloud data that encodes geometry and viewpoint. We demonstrate experimentally on a set of 60 objects captured with stereo cameras that VFH can be used as a distinctive signature, allowing simultaneous recognition of the object and its pose. The pose is accurate enough for robot manipulation, and the computational cost is low enough for real time operation. VFH was designed to be robust to large surface noise and missing depth information in order to work reliably on stereo data.

The Office Marathon: Robust navigation in an indoor office environment

Abstract: This paper describes a navigation system that allowed a robot to complete 26.2 miles of autonomous navigation in a real office environment. We present the methods required to achieve this level of robustness, including an efficient Voxel-based 3D mapping algorithm that explicitly models unknown space. We also provide an open-source implementation of the algorithms used, as well as simulated environments in which our results can be verified.

Efficient Sparse Pose Adjustment for 2D mapping

Abstract: Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.

View-based Maps

Abstract: Robotic systems that can create and use visual maps in real-time have obvious advantages in many applications, from automatic driving to mobile manipulation in the home. In this paper we describe a mapping system based on retaining stereo views of the environment that are collected as the robot moves. Connections among the views are formed by consistent geometric matching of their features. Out-of-sequence matching is the key problem: how to find connections from the current view to other corresponding views in the map. Our approach uses a vocabulary tree to propose candidate views, and a strong geometric filter to eliminate false positives: essentially, the robot continually re-recognizes where it is. We present experiments showing the utility of the approach on video data, including incremental map building in large indoor and outdoor environments, map building without localization, and re-localization when lost.

Contact-reactive grasping of objects with partial shape information

Abstract: Robotic grasping in unstructured environments requires the ability to select grasps for unknown objects and execute them while dealing with uncertainty due to sensor noise or calibration errors. In this work, we propose a simple but robust approach to grasp selection for unknown objects, and a reactive adjustment approach to deal with uncertainty in object location and shape. The grasp selection method uses 3D sensor data directly to determine a ranked set of grasps for objects in a scene, using heuristics based on both the overall shape of the object and its local features. The reactive grasping approach uses tactile feedback from fingertip sensors to execute a compliant robust grasp. We present experimental results to validate our approach by grasping a wide range of unknown objects. Our results show that reactive grasping can correct for a fair amount of uncertainty in the measured position or shape of the objects, and that our grasp selection approach is successful in grasping objects with a variety of shapes.

Combined task and motion planning for mobile manipulation

Abstract: We present a hierarchical planning system and its application to robotic manipulation. The novel features of the system are: 1) it finds high-quality kinematic solutions to task-level problems; 2) it takes advantage of subtask-specific irrelevance information, reusing optimal solutions to state-abstracted sub-problems across the search space. We briefly describe how the system handles uncertainty during plan execution, and present results on discrete problems as well as pick-and-place tasks for a mobile robot.

Autonomous door opening and plugging in with a personal robot

Abstract: We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.

Projected texture stereo

Abstract: Passive stereo vision is widely used as a range sensing technology in robots, but suffers from dropouts: areas of low texture where stereo matching fails. By supplementing a stereo system with a strong texture projector, dropouts can be eliminated or reduced. This paper develops a practical stereo projector system, first by finding good patterns to project in the ideal case, then by analyzing the effects of system blur and phase noise on these patterns, and finally by designing a compact projector that is capable of good performance out to 3m in indoor scenes. The system has been implemented and has excellent depth precision and resolution, especially in the range out to 1.5m.

Depth-encoded hough voting for joint object detection and shape recovery

Abstract: Detecting objects, estimating their pose and recovering 3D shape information are critical problems in many vision and robotics applications This paper addresses the above needs by proposing a new method called DEHV - Depth-Encoded Hough Voting detection scheme Inspired by the Hough voting scheme introduced in [13], DEHV incorporates depth information into the process of learning distributions of image features (patches) representing an object category DEHV takes advantage of the interplay between the scale of each object patch in the image and its distance (depth) from the corresponding physical patch attached to the 3D object DEHV jointly detects objects, infers their categories, estimates their pose, and infers/decodes objects depth maps from either a single image (when no depth maps are available in testing) or a single image augmented with depth map (when this is available in testing) Extensive quantitative and qualitative experimental analysis on existing datasets [6,9,22] and a newly proposed 3D table-top object category dataset shows that our DEHV scheme obtains competitive detection and pose estimation results as well as convincing 3D shape reconstruction from just one single uncalibrated image Finally, we demonstrate that our technique can be successfully employed as a key building block in two application scenarios (highly accurate 6 degrees of freedom (6 DOF) pose estimation and 3D object modeling)

Search-based planning for manipulation with motion primitives

Abstract: Heuristic searches such as A* search are highly popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality and simplicity in the implementation. In planning for robotic manipulation however, these techniques are commonly thought of as impractical due to the high-dimensionality of the planning problem. In this paper, we present a heuristic search-based manipulation planner that does deal effectively with the high-dimensionality of the problem. The planner achieves the required efficiency due to the following three factors: (a) its use of informative yet fast-to-compute heuristics; (b) its use of basic (small) motion primitives as atomic actions; and (c) its use of ARA* search which is an anytime heuristic search with provable bounds on solution suboptimality. Our experimental analysis on a real mobile manipulation platform with a 7-DOF robotic manipulator shows the ability of the planner to solve manipulation in cluttered spaces by generating consistent, low-cost motion trajectories while providing guarantees on completeness and bounds on suboptimality.

Planning for autonomous door opening with a mobile manipulator

Abstract: Computing a motion that enables a mobile manipulator to open a door is challenging because it requires tight coordination between the motions of the arm and the base. Hard-coding the motion, on the other hand, is infeasible since doors vary widely in their sizes and types, some doors are opened by pulling and others by pushing, and indoor spaces often contain obstacles that limit the freedom of the mobile manipulator and the degree to which the doors open up. In this paper, we show how to overcome the high-dimensionality of the planning problem by identifying a graph-based representation that is small enough for efficient planning yet rich enough to contain feasible motions that open doors. The use of graph search-based motion planning enables us to handle consistently the wide variance of conditions under which doors need to be open. We demonstrate our approach on the PR2 robot - a mobile manipulator with an omnidirectional base and a 7 degree of freedom arm. The robot was successful in opening a variety of doors both by pulling and pushing.

A systematic agent framework for situated autonomous systems

Abstract: We present a formal framework of an autonomous agent as a collection of coordinated control loops, with a recurring sense, plan, act cycle. Our framework manages the information flow within the partitioned structure to ensure consistency in order to direct the flow of goals and observations in a timely manner. The resulting control structure improves scalability since many details of each controller can be encapsulated within a single control loop. This partitioned agent design promises a domain-independent, scalable and robust approach for control of real-world autonomous robots operating in dynamic environments. We validate our framework with experimental results from deployments in two different real-world domains.

Task-driven Tactile Exploration

Abstract: This paper presents a decision-theoretic approach to problems that require accurate placement of a robot relative to an object in the world, including grasping and insertion. The decision process is applied to a robot hand with tactile sensors, to localize the object and ultimately achieve a target placement by selecting among grasping and information-gathering trajectories. The process is demonstrated in simulation and on a real robot.

Extending open dynamics engine for robotics simulation

Abstract: Open Dynamics Engine (ODE) is the most popular rigidbody dynamics implementation for robotics simulation applications. While using it to simulate common robotic scenarios like mobile robot locomotion and simple grasping, we have identified the following shortcomings each of which adversely affect robot simulation: lack of computational efficiency, poor support for practical joint-dampening, inadequate solver robustness, and friction approximation via linearization. In this paper we describe extensions to ODE that address each of these problems. Because some of these objectives lie in opposition to others--e.g., speed versus verisimilitude--we have carried out experiments in order to identify the trade-offs involved in selecting from our extensions. Results from both elementary physics and robotic task-based scenarios show that speed improvements can be gained along with useful joint-dampening. If one is willing to accept an execution time cost, we are able to represent the full-friction cone, while simultaneously guaranteeing a solution from our numerical solver.

Judging a bot by its cover: an experiment on expectation setting for personal robots

Abstract: Managing user expectations of personal robots becomes particularly challenging when the end-user just wants to know what the robot can do, and neither understands nor cares about its technical specifications. In describing what a robot can do to such an end-user, we explored the questions of (a) whether or not such users would respond to expectation setting about personal robots and, if so, (b) how such expectation setting would influence human-robot interactions and people's perceptions of the robots. Using a 2 (expectation setting: high vs. low) x 2 (robot type: Pleo vs. AIBO) between-participants experiment (N=24), we examined these questions. We found that people's initial beliefs about the robot's capabilities are indeed influenced by expectation setting tactics. Contrary to the hypotheses predicted by the Self-Fulfilling Prophecy and Confirmation Bias, we found that erring on the side of setting expectations lower rather than higher led to less disappointment and more positive appraisals of the robot's competence.

Sharing Software with ROS [ROS Topics]

Abstract: Robot operating system (ROS) is designed to promote code sharing and enable the development of open-source robotics commons. Sharing code will help the robotics community to progress faster by letting the researchers in the community replicate and extend the results of other research groups. ROS makes it easy to find the software and integrate it into robot systems. This article discusses about how sharing is facilitated by ros.org Web site. ROS contains the software stacks for everything, from building blocks such as controllers and filters to applications. ROS is making it possible for researchers around the world to work together to advance robotics.

Tactile object class and internal state recognition for mobile manipulation

Abstract: Tactile information is valuable in determining properties of objects that are inaccessible from visual perception. In this work, we present a tactile perception strategy that allows any mobile robot with tactile sensors in its gripper to measure a set of generic tactile features while grasping an object. We propose a hybrid velocity-force controller, that grasps an object safely and reveals at the same time its deformation properties. As an application, we show that a robot can use these features to distinguish the open/closed and fill state of bottles and cans - purely from tactile sensing - from a small training set. To prove that this is a hard recognition problem, we also conducted a comperative study with 17 human test subjects. We found that the recognition rate of the human subjects were comparable to our robotic gripper.

Predictors of user perceptions of web recommender systems: How the basis for generating experience and search product recommendations affects user responses

Abstract: One critical question suggested by Web 2.0 is as follows: When is it better to leverage the knowledge of other users vs. rely on the product characteristic-based metrics for online product recommenders? Three recent and notable changes of recommender systems have been as follows: (1) a shift from characteristic-based recommendation algorithms to social-based recommendation algorithms; (2) an increase in the number of dimensions on which algorithms are based; and (3) availability of products that cannot be examined for quality before purchase. The combination of these elements is affecting users' perceptions and attitudes regarding recommender systems and the products recommended by them, but the psychological effects of these trends remain unexplored. The current study empirically examines the effects of these elements, using a 2 (recommendation approach: content-based vs. collaborative-based, within)x2 (dimensions used to generate recommendations: 6 vs. 30, between)x2 (product type: experience products (fragrances) vs. search products (rugs), between) Web-based study (N=80). Participants were told that they would use two recommender systems distinguished by recommendation approach (in fact, the recommendations were identical). There were no substantive main effects, but all three variables exhibited two-way interactions, indicating that design strategies must be grounded in a multi-dimensional understanding of these variables. The implications of this research for the psychology and design of recommender systems are presented.

Low-cost accelerometers for robotic manipulator perception

Abstract: We present a series of experiments which explore the use of consumer-grade accelerometers as joint position sensors for robotic manipulators. We show that 6- and 7-dof joint angle estimation is possible by using one 3-d accelerometer for each pair of joints. We demonstrate two calibration approaches and experimental results using accelerometer-based control in both position-control and torque-control regimes. We present a manipulator design combining accelerometer-based sensing with low-cost actuation, and conclude by demonstrating the utility of consumer-grade accelerometers even on high-precision manipulators.

Welcome to ROS Topics [ROS Topics]

Abstract: Robot operating system (ROS) is a free and opensource system that has grown out of a novel collaboration between industry and academia. This column is designed to introduce you and help track this important community effort. The latest information about ROS will always be available on the Web (http://ros.org). My goal is to help you decide whether or not to download and try the system. In future columns, I'll write about the latest developments in ROS and its progress in the ROS community.

Vision-based detection for learning articulation models of cabinet doors and drawers in household environments

Abstract: Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.

Combining planning techniques for manipulation using realtime perception

Abstract: We present a novel combination of motion planning techniques to compute motion plans for robotic arms. We compute plans that move the arm as close as possible to the goal region using sampling-based planning and then switch to a trajectory optimization technique for the last few centimeters necessary to reach the goal region. This combination allows fast computation and safe execution of motion plans even when the goals are very close to objects in the environment. The system incorporates realtime sensory inputs and correctly deals with occlusions that can occur when robot body parts block the sensor view of the environment. The system is tested on a 7 degree-of-freedom robot arm with sensory input from a tilting laser scanner that provides 3D information about the environment.

User-controlled variable impedance teleoperation

Abstract: Telerobotics fundamentally aims to project human skills into a remote, unstructured environment. A key component of human skills is anticipatory modulation of limb impedances in accordance with task requirements and in expectation of events or disturbances. These adjustments occur continually in human interaction strategies, yet are mostly masked in telerobotics by limited bandwidth controllers and fixed impedance hardware. We propose a telerobotic architecture with user-controlled variable impedance and show a single degree of freedom experimental implementation. The master incorporates a grip force sensor as an additional impedance command channel. Since grip force correlates with the user's own impedance, this input provides an intuitive and natural extension to the regular interface. On the slave, a physically variable clutch actuator is used to adjust both low and high frequency impedance. The additional command channel allows the operator to utilize impedance variation strategies to control impact forces and accomplish varying tasks. These natural interaction strategies are simpler and more robust, leading to superior performance and a telerobot which more effectively represents the operator.

Help me help you: interfaces for personal robots

Abstract: The communication bottleneck between robots and people [1] presents an enormous challenge to the human-robot interaction community. Rather than exclusively focusing on improving robot object learning, task learning, and natural language understanding, we propose also designing interfaces that make up for low communication bandwidth by thoughtfully accounting for the constrained capabilities of robots [2]. People are adept at compensating for communication limitations, changing their communicative strategies for talking to pets, babies [3], foreigners [4], and robots [5]. Communicative accommodation already exists. Thus, instead of requiring robots to perfectly understand natural language, gestures, etc., there is a wide variety of research and design to be done in the space of alternative communicative modalities. We propose to approach this problem by accounting for limitations in robot abilities and taking advantage of already familiar human-computer interaction models, leveraging a communication model based upon Information Theory. Using this design perspective, we present three different mobile user interfaces that were fully developed and implemented on a PR2 (Personal Robot 2) [6] for task domains in navigation, perception, learning and manipulation.

Learning from Demonstration: Communication and Policy Generation

Abstract: Learning from demonstration utilizes human expertise to program a robot. We believe this approach to robot programming will facilitate the development and deployment of general purpose personal robots that can adapt to specific user preferences. Demonstrations can potentially take place across a wide variety of environmental conditions. In this paper we address how learning from demonstration can be affected by various communication alterations. Furthermore, we we detail a Bayesian approach to generating task policies from demonstration data.

On Making Robots Invisible-in-Use

Abstract: A major challenge facing human-robot interaction is un- derstanding how to people will interact and cope with increasingly agentic objects in their everyday lives. As more robotic technolo- gies enter human environments, it is critical to consider other mod- els of human-robot interaction that do not always require focused attention from people. Ubiquitous computing put forth the perspec- tive that computers should not always be the focus of our attention, but that computing should weave itself into the fabric of our ev- eryday lives. Similarly, robots might be the center of attention in some interactions, but might be even more effective when they fade into one's attentional background. In this line of thought, the current study presents results from interviews (N=19) and surveys (N=46) regarding personal experiences with tools that became invisible-in- use, shedding light upon ways that robots might do the same. We present the lessons learned from these open-ended interviews and surveys in the context of larger theories of making tools invisible-in- use (9), functional (16), ready-at-hand (8), proximal (14), and/or in the periphery of one's experience (24).

Multi-DOF model-based force control for telerobotic applications

Abstract: For a wide range of telerobotic applications, the slave device needs to be a large, powerful, industrial type robot in order to achieve the desired tasks. Due to the large frictional forces within the gearing of such robots, a force-feedback controller is necessary to precisely control the forces the robot applies when manipulating its environment. This paper proves passivity, and therefore guarantees stability, of a model-based force controller in one degree of freedom (DOF) when subject to viscous and Coulomb friction. The controller is then expanded to muli-DOF systems. In addition to maintaining the robustness of the 1-DOF controller, the multi-DOF controller provides additional freedom to design the closed loop dynamics of the robot. This freedom allows the control designer the ability to shape and optimize how the system feels from a users perspective. The robustness of the controller is experimentally validated and the freedom to modify the closed loop dynamics is explored using a 2-DOF device.

HRI 2010 workshop 1: what do collaborations with the arts have to say about HRI?

Abstract: Human-Robot Interaction researchers are beginning to reach out to fields not traditionally associated with interaction research, such as the performing arts, cartooning, and animation These collaborations offer the potential for novel insights about how to get robots and people to interact more effectively, but they also involve a number of unique challenges This full-day workshop will offer a venue for HRI researchers and their collaborators from these diverse fields to report on their work, share insights about the collaboration process, and to help begin to define an exciting new area in HRI