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.

Robotic bin management system and method

Abstract: One embodiment is directed to a personal robotic system, comprising: an electromechanical mobile base defining a cross-sectional envelope when viewed in a plane substantially parallel to a plane of a floor upon which the mobile base is operated; a torso assembly movably coupled to the mobile base; a head assembly movably coupled to the torso; a releasable bin-capturing assembly movably coupled to the torso; and a controller operatively coupled to the mobile base, torso assembly, head assembly, and bin-capturing assembly, and configured to capture a bin with the bin-capturing assembly and move the torso assembly relative to the mobile base so that the captured bin fits as closely as possible within the cross-sectional envelope of the mobile base.

FootFall: A Ground Based Operations Toolset Enabling Walking for the ATHLETE Rover

Abstract: The ATHLETE (All-Terrain Hex-Limbed Extra-Terrestrial Explorer) vehicle consists of six identical, six degree of freedom limbs FootFall is a ground tool for ATHLETE intended to provide an operator with integrated situational awareness, terrain reconstruction, stability and safety analysis, motion planning, and decision support capabilities to enable the efficient generation of flight software command sequences for walking FootFall has been under development at NASA Ames for the last year, and having accomplished the initial integration, it is being used to generate command sequences for single footfalls In this paper, the architecture of FootFall in its current state will be presented, results from the recent Human Robotic Systems Project?s Integrated Field Test (Moses Lake, Washington, June, 2008) will be discussed, and future plans for extending the capabilities of FootFall to enable ATHLETE to walk across a boulder field in real time will be described

LiveAction: Automating Web Task Model Generation

Abstract: Task automation systems promise to increase human productivity by assisting us with our mundane and difficult tasks. These systems often rely on people to (1) identify the tasks they want automated and (2) specify the procedural steps necessary to accomplish those tasks (i.e., to create task models). However, our interviews with users of a Web task automation system reveal that people find it difficult to identify tasks to automate and most do not even believe they perform repetitive tasks worthy of automation. Furthermore, even when automatable tasks are identified, the well-recognized difficulties of specifying task steps often prevent people from taking advantage of these automation systems.In this research, we analyze real Web usage data and find that people do in fact repeat behaviors on the Web and that automating these behaviors, regardless of their complexity, would reduce the overall number of actions people need to perform when completing their tasks, potentially saving time. Motivated by these findings, we developed LiveAction, a fully-automated approach to generating task models from Web usage data. LiveAction models can be used to populate the task model repositories required by many automation systems, helping us take advantage of automation in our everyday lives.

Robust Navigation Execution by Planning in Belief Space

Abstract: We consider robot navigation in environments given a known static map, but where dynamic obstacles of varying and unknown lifespans appear and disappear over time We describe a roadmap-based formulation of the problem that takes the sensing and transition uncertainty into account, and an efficient online planner for this problem The planner displays behaviors such as persistence and obstacle timeouts that would normally be hardcoded into an executive It is also able to make inferences about obstacle types even with impoverished sensors We present empirical results on simulated domains and on a PR2 robot

Exploiting segmentation for robust 3D object matching

Abstract: While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beambased models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.