Multi-Finger Grasping Like Humans

TL;DR

This paper presents a novel optimization-based method for transferring human grasp demonstrations to multi-fingered robotic grippers, enabling robots to mimic human-like grasping with improved accuracy and without gripper-specific tuning.

Contribution

The study introduces a new approach for transferring human grasp demonstrations to various robotic grippers, enhancing grasp similarity and reducing the need for gripper-specific adjustments.

Findings

Robotic grasps closely mimic human demonstrations.

The method outperforms existing approaches in grasp similarity.

Validated on real robots and through user studies.

Abstract

Robots with multi-fingered grippers could perform advanced manipulation tasks for us if we were able to properly specify to them what to do. In this study, we take a step in that direction by making a robot grasp an object like a grasping demonstration performed by a human. We propose a novel optimization-based approach for transferring human grasp demonstrations to any multi-fingered grippers, which produces robotic grasps that mimic the human hand orientation and the contact area with the object, while alleviating interpenetration. Extensive experiments with the Allegro and BarrettHand grippers show that our method leads to grasps more similar to the human demonstration than existing approaches, without requiring any gripper-specific tuning. We confirm these findings through a user study and validate the applicability of our approach on a real robot.