Regulating grasping force to reduce slippage during dynamic object interaction remains a fundamental challenge in robotic manipulation, especially when objects are manipulated by multiple rolling contacts, have unknown properties (such as mass or surface conditions), and when external sensing is unreliable. In contrast, humans can quickly regulate grasping force by touch, even without visual cues. Inspired by this ability, we aim to enable robotic hands to rapidly explore objects and learn tactile-driven grasping force control under motion and limited sensing. We propose a physics-informed energy abstraction that models the object as a virtual energy container. The inconsistency between the fingers' applied power and the object's retained energy provides a physically grounded signal for inferring slip-aware stability. Building on this abstraction, we employ model-based learning and planning to efficiently model energy dynamics from tactile sensing and perform real-time grasping force optimization. Experiments in both simulation and hardware demonstrate that our method can learn grasping force control from scratch within minutes, effectively reduce slippage, and extend grasp duration across diverse motion-object pairs, all without relying on external sensing or prior object knowledge.

翻译：在动态物体交互过程中，通过调节抓取力来减少滑移仍然是机器人操作领域的一个基本挑战，特别是在物体通过多个滚动接触被操控、具有未知属性（如质量或表面状况）以及外部传感不可靠的情况下。相比之下，人类即使在没有视觉线索时也能通过触觉快速调节抓取力。受此能力启发，我们的目标是使机器人手能够在运动和有限传感条件下快速探索物体，并学习基于触觉的抓取力控制。我们提出了一种基于物理的能量抽象模型，将物体建模为一个虚拟能量容器。手指施加的功率与物体保留能量之间的不一致性，为推断具有滑移感知的稳定性提供了一个物理基础信号。基于此抽象模型，我们采用基于模型的学习与规划方法，从触觉传感中高效建模能量动力学，并执行实时抓取力优化。仿真与硬件实验均表明，我们的方法能够在数分钟内从零开始学习抓取力控制，有效减少滑移，并在多种运动-物体组合中延长抓持持续时间，且无需依赖外部传感或先验物体知识。