Mind the Gap - Robotic Grasping under Incomplete Observation
We consider the problem of grasp and manipulation planning when the state of the world is only partially observable. Specifically, we address the task of picking up unknown objects from a table top. The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot’s understanding of the world. A completed state estimate of the environment can then be provided to a simulator in which stable grasps and collision-free movements are planned. The proposed approach is based on the observation that many objects commonly in use in a service robotic scenario possess symmetries. We search for the optimal parameters of these symmetries given visibility constraints. Once found, the point cloud is completed and a surface mesh reconstructed. Quantitative experiments show that the predictions are valid approximations of the real object shape. By demonstrating the approach on two very different robotic platforms its generality is emphasized.
Jeannette Bohg Matthew Johnson-Roberson Beatriz Le(o)n Javier Felip Xavi Gratal Niklas Bergstr(o)m Danica Kragic Antonio Morales
CVAP/ CAS at KTH,Stockholm,Sweden Robotic Intelligence Laboratory at the Department of Computer Science and Engineering,UJI,Castell(o)
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
686-693
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）