无需预训练，仅靠运动规划实现高精度大范围抓持内操作
Robotic In-Hand Manipulation for Large-Range Precise Object Movement: The RGMC Champion Solution
- 基于运动学轨迹优化，不依赖物体几何或预训练
- 在ICRA 2024 RGMC竞赛中夺得抓持内操作冠军
- 适用于真实场景中的新物体，部署简单实用
使用多个灵巧手指进行抓持内操作是关键的机器人技能，可减少对大型臂部运动的依赖，从而节省空间与能耗。本文聚焦于抓持内物体位姿调整，即通过手指运动在稳定抓握下将物体移动到目标姿态。核心挑战在于同时实现高精度与大范围运动，且保持抓握稳定。为此，我们提出一种无需预训练或物体几何信息的简单实用方法，基于运动学轨迹优化，可直接应用于现实场景中的新物体。采用该方法，我们在ICRA 2024举办的第九届机器人抓取与操作竞赛（RGMC）抓持内操作赛道中夺得冠军。本文详细阐述实现细节、讨论及进一步定量实验结果，旨在全面评估该方法，并分享竞赛关键经验。补充材料（含视频与代码）见 https://rgmc-xl-team.github.io/ingrasp_manipulation。
原文摘要 · Abstract (English)
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 .
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