Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection
Lirui Wang, Xiang Yu, Dieter Fox
Abstract
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipulator to avoid obstacles in the environment and plan an end-effector pose for grasping. While trajectory planning and grasp planning are often tackled separately, how to efficiently integrate the two planning problems remains a challenge. In this work, we present a novel method for joint motion and grasp planning. Our method integrates manipulation trajectory optimization with online grasp synthesis and selection, where we apply online learning techniques to select goal configurations for grasping, and introduce a new grasp synthesis algorithm to generate grasps online. We evaluate our planning approach and demonstrate that our method generates robust and efficient motion plans for grasping objects in cluttered scenes.