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.

Robots for humanity: using assistive robotics to empower people with disabilities

Abstract: Assistive mobile manipulators (AMMs) have the potential to one day serve as surrogates and helpers for people with disabilities, giving them the freedom to perform tasks such as scratching an itch, picking up a cup, or socializing with their families.

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.

Detecting and tracking people using an RGB-D camera via multiple detector fusion

Abstract: The goal of personal robotics is to create machines that help us with the tasks of daily living, co-habiting with us in our homes and offices. These robots must interact with people on a daily basis, navigating with and around people, and approaching people to serve them. To enable this coexistence, personal robots must be able to detect and track people in their environment. Excellent progress has been made in the vision community in detecting people outdoors, in surveillance scenarios, in Internet images, or in specific scenarios such as video game play in living rooms. The indoor robot perception problem differs, however, in that the platform is moving, the subjects are frequently occluded or truncated by the field-of-view, there is large scale variation, the subjects take on a wider range of poses than pedestrians, and computation must take place in near real time. In this paper, we describe a system for detecting and tracking people from image and depth sensors on board a mobile robot. To cope with the challenges of indoor mobile perception, our system combines an ensemble of detectors in a unified framework, is efficient, and has the potential to incorporate multiple sensor inputs. The performance of our algorithm surpasses other approaches on two challenging data sets, including a new robot-based data set.

Physics-Based Grasp Planning Through Clutter

Abstract: We propose a planning method for grasping in cluttered environments, where the robot can make simultaneous contact with multiple objects, manipulating them in a deliberate and controlled fashion. This enables the robot to reach for and grasp the target while simultaneously contacting and moving aside obstacles in order to clear a desired path. We use a physicsbased analysis of pushing to compute the motion of each object in the scene in response to a set of possible robot motions. In order to make the problem computationally tractable, we enable multiple simultaneous robot-object interactions, which we pre-compute and cache, but avoid object-object interactions. Tests on large sets of simulated scenes show that our planner produces more successful grasps in more complex scenes than versions that avoid any interaction with surrounding clutter. Validation on a real robot shows that our grasp evaluation method accurately predicts the outcome of a grasp, and that our approach, in conjunction with state-of-the-art object recognition tools, is applicable in reallife scenes that are highly cluttered and constrained.

Navigation in hybrid metric-topological maps

Abstract: We present an approach for navigation in hybrid maps consisting of a topological graph overlaid with local occupancy grids. The topological graph is built on top of a graph SLAM system, which can be efficiently optimized even for very large environments. The novel feature of our system is that it navigates locally using local metric maps, while the overall plan is formed on the topological graph. Unlike many current SLAM methods, we never reconstruct a full occupancy grid of the environment for localization or path planning. We show that our method generates near-optimal plans, and deals gracefully with changes to the map.