2017/10/30 by Nikhil Chavan-Dafle, Alberto Rodriguez, Chavan-Dafle, Nikhil +2 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.1710.11097
IEEE International Conference on Robotics and Automation 2018
openalex publication_date 2017/10/30 · arxiv created 2018/03/04 · arxiv updated 2018/03/06 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
This paper presents an approach to in-hand manipulation planning that exploits the mechanics of alternating sticking contact. Particularly, we consider the problem of manipulating a grasped object using external pushes for which the pusher sticks to the object. Given the physical properties of the object, frictional coefficients at contacts and a desired regrasp on the object, we propose a sampling-based planning framework that builds a pushing strategy concatenating different feasible stable pushes to achieve the desired regrasp. An efficient dynamics formulation allows us to plan in-hand manipulations 100-1000 times faster than our previous work which builds upon a complementarity formulation. Experimental observations for the generated plans show that the object precisely moves in the grasp as expected by the planner. Video Summary -- youtu.be/qOTKRJMx6Ho