Relaxed-Rigidity Constraints: Kinematic Trajectory Optimization and Collision Avoidance for In-Grasp Manipulation

TL;DR

This paper introduces a kinematic trajectory optimization method for in-grasp manipulation that avoids object damage and collisions without needing dynamic object data, demonstrated on a robotic hand with diverse objects.

Contribution

The paper presents a novel kinematic trajectory optimization approach for in-grasp manipulation that incorporates collision avoidance and joint smoothness, applicable to various objects without dynamic modeling.

Findings

Successful manipulation of 10 YCB objects demonstrating versatility.

Effective collision avoidance using signed distance cost functions.

Robustness to unmodeled dynamics via smooth joint trajectories.

Abstract

This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on 10 objects from the YCB dataset. However, the proposed method is general enough to generate motions for most objects the robot can grasp. Experimental result support the feasibillty of its application across a variety of object shapes. We explore the adaptability of our approach to additional task requirements by including collision avoidance and joint space smoothness costs. The grasped object avoids collisions with the environment by the use of a signed distance cost function. We reduce the effects of unmodeled object dynamics by requiring smooth joint trajectories. We additionally compensate for errors encountered during trajectory execution by formulating an object pose feedback controller.