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通常表现出较大的模拟与现实差距。例如，工业装配任务常涉及紧密插装，其间隙小于\(0.1\)毫米，在处理可变形承窝时甚至可能为负间隙。这种狭窄间隙导致复杂的接触动力学，难以在仿真中精确建模，从而使得将在模拟中学习的策略迁移到真实机器人变得具有挑战性。本文提出了一种仅使用模拟数据稳健学习现实世界操作技能的新框架。该框架由两个主要组件组成：“力规划器”和“增益调谐器”。力规划器负责规划机器人运动及期望接触力，而增益调谐器则动态调整顺应控制增益，以在任务执行过程中精确跟踪期望接触力。本研究的关键见解在于：通过在任务执行过程中自适应调整机器人的顺应控制增益，我们能够在新环境中调节接触力，从而生成与仿真训练轨迹相似的路径，缩小模拟与现实的差距。实验结果表明，我们的方法在通用方形插销-孔任务上通过仿真训练后，无需任何微调即可泛化至涉及窄间隙甚至负间隙的多种真实世界插装任务。