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.

Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution

Abstract: The success of personal service robotics hinges upon reliable manipulation of everyday household objects, such as dishes, bottles, containers, and furniture. In order to accurately manipulate such objects, robots need to know objects’ full 6-DOF pose, which is made difficult by clutter and occlusions. Many household objects have regular structure that can be used to effectively guess object pose given an observation of just a small patch on the object. In this paper, we present a new method to model the spatial distribution of oriented local features on an object, which we use to infer object pose given small sets of observed local features. The orientation distribution for local features is given by a mixture of Binghams on the hypersphere of unit quaternions, while the local feature distribution for position given orientation is given by a locally-weighted (Quaternion kernel) likelihood. Experiments on 3D point cloud data of cluttered and uncluttered scenes generated from a structured light stereo image sensor validate our approach.

Robot Web Tools: Efficient messaging for cloud robotics

Abstract: Since its official introduction in 2012, the Robot Web Tools project has grown tremendously as an open-source community, enabling new levels of interoperability and portability across heterogeneous robot systems, devices, and front-end user interfaces. At the heart of Robot Web Tools is the rosbridge protocol as a general means for messaging ROS topics in a client-server paradigm suitable for wide area networks, and human-robot interaction at a global scale through modern web browsers. Building from rosbridge, this paper describes our efforts with Robot Web Tools to advance: 1) human-robot interaction through usable client and visualization libraries for more efficient development of front-end human-robot interfaces, and 2) cloud robotics through more efficient methods of transporting high-bandwidth topics (e.g., kinematic transforms, image streams, and point clouds). We further discuss the significant impact of Robot Web Tools through a diverse set of use cases that showcase the importance of a generic messaging protocol and front-end development systems for human-robot interaction.

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.

Single- and dual-arm motion planning with heuristic search

Abstract: Heuristic searches such as the 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. Our approach achieves the necessary efficiency by exploiting the following three key principles: (a) representation of the planning problem with what we call a manipulation lattice graph; (b) use of the ARA* search which is an anytime heuristic search with provable bounds on solution suboptimality; and (c) use of informative yet fast-to-compute heuristics. The paper presents the approach together with its theoretical properties and shows how to apply it 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 a par with other most common sampling-based approaches despite the high dimensionality of the problems. In addition, the experimental analysis shows that due to its deterministic cost minimization, the approach generates motions that are of good quality and are consistent, in other words, the resulting plans tend to be similar for similar tasks. For many problems, the consistency of the generated motions is important as it helps make the actions of the robot more predictable for a human controlling or interacting with the robot.

ROS commander (ROSCo): Behavior creation for home robots

Abstract: We introduce ROS Commander (ROSCo), an open source system that enables expert users to construct, share, and deploy robot behaviors for home robots. A user builds a behavior in the form of a Hierarchical Finite State Machine (HFSM) out of generic, parameterized building blocks, with a real robot in the develop and test loop. Once constructed, users save behaviors in an open format for direct use with robots, or for use as parts of new behaviors. When the system is deployed, a user can show the robot where to apply behaviors relative to fiducial markers (AR Tags), which allows the robot to quickly become operational in a new environment. We show evidence that the underlying state machine representation and current building blocks are capable of spanning a variety of desirable behaviors for home robots, such as opening a refrigerator door with two arms, flipping a light switch, unlocking a door, and handing an object to someone. Our experiments show that sensor-driven behaviors constructed with ROSCo can be executed in realistic home environments with success rates between 80% and 100%. We conclude by describing a test in the home of a person with quadriplegia, in which the person was able to automate parts of his home using previously-built behaviors.