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

Mingrui Yu, Yongpeng Jiang, Chen Chen, Yongyi Jia, Xiang Li

Keywords: Multi-fingered in-hand manipulation, Trajectory optimization, Robotic Grasping and Manipulation Competition.

TL;DR: We propose a simple and practical approach to precise in-grasp manipulation, which won the championship of ICRA 2024 RGMC (in-hand manipulation track).

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 work 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.

Submission Number: 9