Willow Garage

About: Willow Garage is a based out in . It is known for research contribution in the topics: Robot & Mobile robot. The organization has 76 authors who have published 191 publications receiving 28617 citations.

Planning for Manipulation with Adaptive Motion Primitives

Abstract: In this paper, we present a search-based motion planning algorithm for manipulation that handles the high dimensionality of the problem and minimizes the limitations associated with employing a strict set of pre-defined actions. Our approach employs a set of adaptive motion primitives comprised of static motions with variable dimensionality and on-the-fly motions generated by two analytical solvers. This method results in a slimmer, multi-dimensional lattice and offers the ability to satisfy goal constraints with precision. To validate our approach, we used a 7DOF manipulator to perform experiments on a real mobile manipulation platform (Willow Garage's PR2). Our results demonstrate the effectiveness of the planner in efficiently navigating cluttered spaces; the method generates consistent, low-cost motion trajectories, and guarantees the search is complete with bounds on the suboptimality of the solution.

Laser-based perception for door and handle identification

Abstract: In this paper, we present a laser-based approach for door and handle identification. The approach builds on a 3D perception pipeline to annotate doors and their handles solely from sensed laser data, without any a priori model learning. In particular, we segment the parts of interest using robust geometric estimators and statistical methods applied on geometric and intensity distribution variations in the scan. We present experimental results on a mobile manipulation platform (PR2) intended for indoor manipulation tasks. We validate the approach by generating trajectories that position the robot end-effector in front of door handles and grasp the handle. The robustness of our approach is demonstrated by real world experiments conducted on a large set of doors.

Can You See Me Now?: How Field of View Affects Collaboration in Robotic Telepresence

Abstract: Robotic telepresence systems-videoconferencing systems that allow a remote user to drive around in another location-are an emerging technology for supporting geographically-distributed teams. Thus far, many of these systems rely on affordances designed for stationary systems, such as a single, narrow-view camera to provide vision for the remote user. Teleoperation has offered some solutions to this via an augmented field-of-view, but how these solutions support task outcomes in collaborative mobile telepresence tasks has yet to be understood. To investigate this, we conducted a three condition (field-of-view: narrow (45°) vs. wide-angle (180°) vs. panoramic (360°)) between-participants controlled laboratory experiment. We asked participants (N=24) to collaborate with a confederate via a robotic telepresence system while using one of these views in a redecoration task. Our results showed that wider views supported task efficiency and fewer collisions, but were perceived as more difficult to use.

Robust grasping under object pose uncertainty

Abstract: This paper presents a decision-theoretic approach to problems that require accurate placement of a robot relative to an object of known shape, such as grasping for assembly or tool use. The decision process is applied to a robot hand with tactile sensors, to localize the object on a table and ultimately achieve a target placement by selecting among a parameterized set of grasping and information-gathering trajectories. The process is demonstrated in simulation and on a real robot. This work has been previously presented in Hsiao et al. (Workshop on Algorithmic Foundations of Robotics (WAFR), 2008; Robotics Science and Systems (RSS), 2010) and Hsiao (Relatively robust grasping, Ph.D. thesis, Massachusetts Institute of Technology, 2009).

Single- and dual-arm motion planning with heuristic search

Abstract: Heuristic searches such as A* search are a popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality, simplicity in implementation and consistent behavior. 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 approach to motion planning for manipulation that does deal effectively with the high-dimensionality of the problem. The paper presents a summary of the approach along with applications to single-arm and dual-arm motion planning with upright constraints on a PR2 robot operating in non-trivial cluttered spaces. An extensive experimental analysis in both simulation and on a physical PR2 shows that, in terms of runtime, our approach is on par with other most common sampling-based approaches and due to its deterministic cost-minimization, the computed motions are of good quality and are consistent, i.e. the resulting plans tend to be similar for similar tasks. For complete details of our approach, please refer to (Cohen, Chitta, and Likhachev 2013).