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Institution

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.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
05 Mar 2012
TL;DR: Using the technique of need finding, a group of people are interviewed regarding the reality of organization in their home; the successes, failures, family dynamics and practicalities surrounding organization are abstracted into a set of frameworks and design implications for home robotics.
Abstract: Technologists have long wanted to put robots in the home, making robots truly personal and present in every aspect of our lives. It has not been clear, however, exactly what these robots should do in the home. The difficulty of tasking robots with home chores comes not only from the significant technical challenges, but also from the strong emotions and expectations people have about their home lives. In this paper, we explore one possible set of tasks a robot could perform, home organization and storage tasks. Using the technique of need finding, we interviewed a group of people regarding the reality of organization in their home; the successes, failures, family dynamics and practicalities surrounding organization. These interviews are abstracted into a set of frameworks and design implications for home robotics, which we contribute to the community as inspiration and hypotheses for future robot prototypes to test.

41 citations

Proceedings ArticleDOI
03 May 2010
TL;DR: An approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers using a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system.
Abstract: Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.

39 citations

Proceedings ArticleDOI
06 May 2013
TL;DR: This paper presents two novel techniques to accelerate the computation of broad-phase data structures: a progressive technique that incrementally computes a high-quality dynamic AABB tree for fast culling and an octree representation of the point cloud data as a proximity data structure.
Abstract: Most prior techniques for proximity computations are designed for synthetic models and assume exact geometric representations. However, real robots construct representations of the environment using their sensors, and the generated representations are more cluttered and less precise than synthetic models. Furthermore, this sensor data is updated at high frequency. In this paper, we present new collision- and distance-query algorithms, which can efficiently handle large amounts of point cloud sensor data received at real-time rates. We present two novel techniques to accelerate the computation of broad-phase data structures: 1) we present a progressive technique that incrementally computes a high-quality dynamic AABB tree for fast culling, and 2) we directly use an octree representation of the point cloud data as a proximity data structure. We assign a probability value to each leaf node of the tree, and the algorithm computes the nodes corresponding to high collision probability. In practice, our new approaches can be an order of magnitude faster than previous methods. We demonstrate the performance of the new methods on both synthetic data and on sensor data collected using a Kinect™ for motion planning for a mobile manipulator robot.

39 citations

Proceedings ArticleDOI
14 May 2012
TL;DR: This paper presents a search-based approach that is capable of planning dual-arm motions in cluttered environments while adhering to the orientation constraints and generates motions that are consistent across runs with similar start/goal configurations and are low-cost.
Abstract: Dual-arm manipulation is an increasingly important skill for robots operating in home, retail and industrial environments. Dual-arm manipulation is especially essential for tasks involving large objects which are harder to grasp and manipulate using a single arm. In this work, we address dual-arm manipulation of objects in indoor environments. We are particularly focused on tasks that involve an upright orientation constraint on the grasped object. Such constraints are often present in human environments, e.g. when manipulating a tray of food or a container with fluids. In this paper, we present a search-based approach that is capable of planning dual-arm motions, often within one second, in cluttered environments while adhering to the orientation constraints. Our approach systematically constructs a graph in task space and generates motions that are consistent across runs with similar start/goal configurations and are low-cost. These motions come with guarantees on completeness and bounds on the suboptimality with respect to the graph that encodes the planning problem. 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 interacting with the robot.

38 citations

Proceedings ArticleDOI
03 Mar 2014
TL;DR: This paper develops a system that allows users to program complex manipulation skills on a two-armed robot through a spoken dialog interface and by physically moving the robot’s arms, and investigates the effect of providing users with an additional written tutorial or an instructional video.
Abstract: Allowing end-users to harness the full capability of general purpose robots, requires giving them powerful tools. As the functionality of these tools increase, learning how to use them becomes more challenging. In this paper we investigate the use of instructional materials to support the learnability of a Programming by Demonstration tool. We develop a system that allows users to program complex manipulation skills on a two-armed robot through a spoken dialog interface and by physically moving the robot’s arms. We present a user study (N=30) in which participants are left alone with the robot and a user manual, without any prior instructions on how to program the robot. Instead, they are asked to figure it out on their own. We investigate the effect of providing users with an additional written tutorial or an instructional video. We find that videos are most effective in training the user; however, this effect might be superficial and ultimately trial-and-error plays an important role in learning to program the robot. We also find that tutorials can be problematic when the interaction has uncertainty due to speech recognition errors. Overall, the user study demonstrates the effectiveness and learnability of the our system, while providing useful feedback about the dialog design.

37 citations


Authors

Showing all 76 results

NameH-indexPapersCitations
Ian Goodfellow85137135390
Kurt Konolige6417124749
Andreas Paepcke501409405
Gunter Niemeyer4715317135
Radu Bogdan Rusu439715008
Mike J. Dixon421828272
Gary Bradski418223763
Leila Takayama34904549
Sachin Chitta34564589
Wendy Ju341843861
Maya Cakmak341114452
Brian P. Gerkey32517923
Caroline Pantofaru26654116
Matei Ciocarlie25913176
Kaijen Hsiao24292366
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20172
20164
20152
201414
201336
201239