Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection

TL;DR

This paper introduces a novel approach for joint manipulation trajectory optimization and online grasp synthesis, enabling robots to efficiently plan and execute grasping motions in cluttered environments by integrating online learning and grasp generation techniques.

Contribution

The work presents a new method that combines manipulation trajectory optimization with online grasp synthesis and selection, addressing the integration challenge in robotic grasp planning.

Findings

Generates robust and efficient grasping motion plans in cluttered scenes.

Integrates online learning for goal configuration selection.

Introduces a new online grasp synthesis algorithm.

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 arm 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 in cluttered scenes. Our video can be found at https://www.youtube.com/watch?v=LIcACf8YkGU .