FINGR：学习真实世界魔方解算的灵巧手控制

中文摘要 AI 辅助

用单只灵巧手操纵魔方是对持续、接触丰富控制的一项挑战性测试：手必须在保持魔方稳固的同时执行连续的层转动。每次转动都需要一些手指支撑魔方，而其他手指推动移动层、释放接触并为下一步重置。为了学习这种协调，我们引入了FINGR（带几何表示的未来监督交互网络），这是一种结合手指相对几何与未来交互预测的策略。一个共享的点编码器将魔方相对于每个指尖表示，并聚合其点而不依赖于方块索引。学习到的未来令牌共享观测编码器，并在多个时间尺度上接收接触力变化、层转动进度和手指关节位移的监督。由此产生的表示条件化一个直接生成手指动作的流策略。在真实灵巧手上，我们的策略在300次转动尝试中实现了99.0%的成功率，而基础流策略为79.7%。与抓取和桌面辅助重抓取集成后，该策略在平均完整系统时间约137秒内解决了所有十个打乱的$2\ imes2\ imes2$魔方。项目网站可在此https URL访问。

英文摘要

Manipulating a Rubik's Cube with a single dexterous hand is a challenging test of sustained, contact-rich control: the hand must execute successive layer turns while keeping the cube secure. Each turn requires some fingers to support the cube while others push a moving layer, release contact, and reset for the next move. To learn this coordination, we introduce FINGR (Future-supervised Interaction Network with Geometric Representations), a policy that combines finger-relative geometry with future interaction prediction. A shared point encoder expresses the cube relative to each fingertip and aggregates its points without depending on cubie indexing. Learned future tokens share the observation encoder and receive supervision for contact-force changes, layer-turn progress, and finger joint displacement at multiple time scales. The resulting representation conditions a flow policy that directly generates finger actions. On a real dexterous hand, our policy achieves 99.0% success over 300 turn attempts, compared with 79.7% for the base flow policy. Integrated with grasping and table-assisted regrasping, the policy solves all ten scrambled $2\times2\times2$ cubes in a mean complete-system time of approximately 137 seconds. The project website is available at https://www.lyt0112.com/projects/FINGR

发表机构

• University of California San Diego（加州大学圣迭戈分校）

机构由 AI 辅助整理，请以论文原文为准。