Blind Grasping: Stable Robotic Grasping Using Tactile Feedback and Hand Kinematics
We propose a machine learning approach to the perception of a stable robotic grasp based on tactile feedback and hand kinematic data, which we call blind grasping. We first discuss a method for simulating tactile feedback using a soft finger contact model in GraspIt!, which is a robotic grasping simulator 10. Using this simulation technique, we compute tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database 6. The tactile contacts along with the hand kinematic data are then input to a Support Vector Machine (SVM) which is trained to estimate the stability of a given grasp based on this tactile feedback and also the robotic hand kinematics. Experimental results indicate that the tactile feedback along with the hand kinematic data carry meaningful information for the prediction of the stability of a blind robotic grasp.
Hao Dang Jonathan Weisz Peter K. Allen
Department of Computer Science,Columbia University,450 Computer Science Building,1214 Amsterdam Avenue,New York,NY,10027,USA
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
5917-5922
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）