发表机构
Center for Robotics and Biosystems, Northwestern University（西北大学机器人与生物系统中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究无外部传感的手中操作难题，引入全局抓握质量先验和局部接触几何先验两种互补物理先验，用于多指机器人手操作物体的手中滚动操作学习，显著提升了旋转效率、抓握稳定性和抗干扰能力，增强任务稳健性与模拟到现实的转移。
AI 中文摘要
由于手指与物体接触的不确定性和重力干扰，无外部传感的手中操作具有挑战性。虽然强化学习在学习复杂手指动作方面有前景，但现有方法未优先维持良好条件的抓握以进行持续操作。我们引入两种互补的物理先验用于稳健的手中滚动：源自经典抓握分析的全局抓握质量先验和基于指尖曲率的局部接触几何先验。抓握质量先验用作密集奖励塑造项，鼓励分布良好的接触并提高最坏情况的扳手阻力。接触几何先验以指尖几何表示，机械地塑造接触界面以实现任务对齐的滚动，同时减少离轴漂移。我们评估了这些先验对多指机器人手在四个手掌方向操作三个不同物体的手中滚动操作学习的影响。结果表明在旋转效率、抓握稳定性和抗干扰方面有显著改进，表明嵌入学习和指尖形态学的物理先验提高了任务稳健性和从模拟到现实的转移。
英文摘要
In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity. While reinforcement learning has shown promise in learning complex finger gaiting, existing approaches do not prioritize maintaining well-conditioned grasps for sustained manipulation. We introduce two complementary physics priors for robust in-hand rolling: a global grasp-quality prior derived from classical grasp analysis and a local contact-geometry prior based on fingertip curvature. The grasp-quality prior is used as a dense reward-shaping term that encourages well-distributed contacts with improved worst-case wrench resistance. The contact-geometry prior is expressed in the fingertip geometry that mechanically shapes the contact interface toward task-aligned rolling while reducing off-axis drift. We evaluate the effect of these priors on learning in-hand rolling manipulation for a multifingered robotic hand manipulating three different objects at four palm orientations. Results show significant improvement in rotation efficiency, grasp stability, and disturbance rejection, suggesting that physics priors embedded in both learning and fingertip morphology improve task robustness and sim-to-real transfer. An overview video can be found at https://youtu.be/pdd1wHxQnJM?si=dM-U5kiiPTYsk3Pk.
Comments25 pages, 15 figures, 9 tables
