Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution

TL;DR

This paper presents a kinematic trajectory optimization approach for in-hand robotic manipulation that achieves high-precision, large-range object movements within a stable grasp, winning the RGMC competition.

Contribution

The authors introduce a simple, practical method for in-grasp object manipulation that does not require pretraining or object models, applicable to novel objects in real-world scenarios.

Findings

Won the RGMC in-hand manipulation competition at ICRA 2024.

Achieved high-precision, large-range object movements with stable grasp.

Demonstrated effectiveness on novel objects without pretraining.

Abstract

In-hand manipulation using multiple dexterous fingers is a critical robotic skill that can reduce the reliance on large arm motions, thereby saving space and energy. This letter focuses on in-grasp object movement, which refers to manipulating an object to a desired pose through only finger motions within a stable grasp. The key challenge lies in simultaneously achieving high precision and large-range movements while maintaining a constant stable grasp. To address this problem, we propose a simple and practical approach based on kinematic trajectory optimization with no need for pretraining or object geometries, which can be easily applied to novel objects in real-world scenarios. Adopting this approach, we won the championship for the in-hand manipulation track at the 9th Robotic Grasping and Manipulation Competition (RGMC) held at ICRA 2024. Implementation details, discussion, and further quantitative experimental results are presented in this letter, which aims to comprehensively evaluate our approach and share our key takeaways from the competition. Supplementary materials including video and code are available at https://rgmc-xl-team.github.io/ingrasp_manipulation .