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.

Assisted driving of a mobile remote presence system: System design and controlled user evaluation

Abstract: As mobile remote presence (MRP) systems become more pervasive in everyday environments such as office spaces, it is important for operators to navigate through remote locations without running into obstacles. Human-populated environments frequently change (e.g., doors open and close, furniture is moved around) and mobile remote presence systems must be able to adapt to such changes and to avoid running into obstacles. As such, we implemented an assisted teleoperation feature for a MRP system and evaluated its effectiveness with a controlled user study, focusing on both the system-oriented dimensions (e.g., autonomous assistance vs. no assistance) and human-oriented dimensions (e.g., gaming experience, locus of control, and spatial cognitive abilities) (N=24). In a systems-only analysis, we found that the assisted teleoperation helped people avoid obstacles. However, assisted teleoperation also increased time to complete an obstacle course. When human-oriented dimensions were evaluated, gaming experience and locus of control affected speed of completing the course. Implications for future research and design are discussed.

An object-based semantic world model for long-term change detection and semantic querying

Abstract: Recent years have seen rising interest in robotic mapping algorithms that operate at the level of objects, rather than two- or three-dimensional occupancy. Such “semantic maps” permit higher-level reasoning than occupancy maps, and are useful for any application that involves dealing with objects, including grasping, change detection, and object search. We describe and experimentally verify such a system aboard a mobile robot equipped with a Microsoft Kinect RGB-D sensor. Our representation is object-based, and makes uniquely weak assumptions about the quality of the perceptual data available; in particular, we perform no explicit object recognition. This allows our system to operate in large, dynamic, and uncon-strained environments, where modeling every object that occurs (or might occur) is impractical. Our dataset, which is publicly available, consists of 67 autonomous runs of our robot over a six-week period in a roughly 1600m2 office environment. We demonstrate two applications built on our system: semantic querying and change detection.

Learning kinematic models for articulated objects

Abstract: Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.

Collaborative grasp planning with multiple object representations

Abstract: Grasp planning based on perceived sensor data of an object can be performed in different ways, depending on the chosen semantic interpretation of the sensed data. For example, if the object can be recognized and a complete 3D model is available, a different planning tool can be selected compared to the situation in which only the raw sensed data, such as a single point cloud, is available. Instead of choosing between these options, we present a framework that combines them, aiming to find consensus on how the object should be grasped by using the information from each object representation according to their confidence levels. We show that this method is robust to common errors in perception, such as incorrect object recognition, while also taking into account potential grasp execution errors due to imperfect robot calibration. We illustrate this method on the PR2 robot by grasping objects common in human environments.

Kinetic and dimensional optimization for a tendon-driven gripper

Abstract: We design, optimize and demonstrate the behavior of a tendon-driven robotic gripper performing fingertip and enveloping grasps. The gripper consists of two fingers, each with two links, and is actuated using a single active tendon. During unobstructed closing, the distal links remain parallel, creating exact fingertip grasps. Conversely, if the proximal links are stopped by contact with an object, the distal links start flexing, creating a stable enveloping grasp. We optimize the route of the active tendon and the parameters of the springs providing passive extension forces in order to achieve this behavior. We show how an additional passive tendon can be used as a constraint preventing the gripper from entering undesirable parts of the joint workspace. Finally, we introduce a method for optimizing the dimensions of the links in order to achieve enveloping grasps of a large range of objects, and apply it to a set of common household objects.