In-Hand Manipulation of Articulated Tools with Dexterous Robot Hands with Sim-to-Real Transfer

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Reinforcement learning (RL) and sim-to-real transfer have advanced rigid-object manipulation. However, policies remain brittle for articulated mechanisms due to contact-rich dynamics that require both stable grasping and simultaneous free in-hand articulation. Furthermore, articulated objects and robot hands exhibit under-modeled joint phenomena such as friction, stiction, and backlash in real life that can increase the sim-to-real gap, and robot hands still fall short of idealized tactile sensing, both in terms of coverage, sensitivity, and specificity. In this paper, we present an original approach to learning dexterous in-hand manipulation of articulated tools that has reduced articulation and kinematic redundancy relative to the human hand. Our approach augments a simulation-trained base policy with a sensor-driven refinement learned from hardware demonstrations. This refinement conditions on proprioception and target articulation states while fusing whole-hand tactile and force-torque feedback with the policy's action intent through cross-attention. The resulting controller adapts online to instance-specific articulation properties, stabilizes contact interactions, and regulates internal forces under perturbations. We validate our method across diverse real-world tools, including scissors, pliers, minimally invasive surgical instruments, and staplers, demonstrating robust sim-to-real transfer, improved disturbance resilience, and generalization across structurally related articulated tools without precise physical modeling.

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