Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For instance, industrial assembly tasks frequently involve tight insertions where the clearance is less than \(0.1\) mm and can even be negative when dealing with a deformable receptacle. This narrow clearance leads to complex contact dynamics that are difficult to model accurately in simulation, making it challenging to transfer simulation-learned policies to real-world robots. In this paper, we propose a novel framework for robustly learning manipulation skills for real-world tasks using only the simulated data. Our framework consists of two main components: the ``Force Planner'' and the ``Gain Tuner''. The Force Planner is responsible for planning both the robot motion and desired contact forces, while the Gain Tuner dynamically adjusts the compliance control gains to accurately track the desired contact forces during task execution. The key insight of this work is that by adaptively adjusting the robot's compliance control gains during task execution, we can modulate contact forces in the new environment, thereby generating trajectories similar to those trained in simulation and narrows the sim-to-real gap. Experimental results show that our method, trained in simulation on a generic square peg-and-hole task, can generalize to a variety of real-world insertion tasks involving narrow or even negative clearances, all without requiring any fine-tuning.

翻译：接触丰富的操作任务往往存在较大的仿真到现实差距。例如，工业装配任务常涉及间隙小于0.1毫米的紧密插接，甚至在处理可变形承接件时可能出现负间隙。这种狭窄间隙导致复杂的接触动力学特性难以在仿真中精确建模，使得将仿真习得的策略迁移至真实机器人面临挑战。本文提出一种仅利用仿真数据即可鲁棒学习真实世界操作技能的新型框架。该框架包含两个核心组件："力规划器"与"增益调节器"。力规划器负责规划机器人运动轨迹与期望接触力，而增益调节器则动态调整顺应性控制增益，以在任务执行过程中精确跟踪期望接触力。本研究的关键洞见在于：通过自适应调整机器人任务执行过程中的顺应性控制增益，能够在新环境中调节接触力，从而生成与仿真训练中相似的轨迹，有效缩小仿真到现实差距。实验结果表明，本方法在通用方形销孔插接任务的仿真环境中训练后，能够泛化至涉及狭窄甚至负间隙的多种真实插接任务，且无需任何微调。