让机器人在无传感器情况下精准感知并控制接触力，实现柔顺操作。
FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks
- 双环结构融合模仿学习与阻抗控制，实现力感知与力执行
- 仅用关节扭矩估算末端受力，误差低于1.2N，性能媲美真实传感器
- 适合低成本机器人做高精度接触任务，如装配、打磨
许多需要精确力调控的接触型操作任务难以实现。现有模仿学习策略多以位置为中心，缺乏显式力意识，且为协作机械臂加装力/力矩传感器常成本高昂，需额外硬件设计。为此，我们提出FILIC：一种基于阻抗力矩控制的力引导模仿学习框架。FILIC采用双环结构，将基于Transformer的模仿学习策略与阻抗控制器结合，实现顺应性、力感知、力执行的操作。对于无力传感器的机器人，我们提出一种低成本末端力估计算法，通过关节扭矩测量与解析雅可比逆推算，并利用数字孪生模型预测补偿扭矩。实验表明，FILIC显著优于仅依赖视觉或关节扭矩的方法，在安全性、顺应性和适应性上表现更优，误差低于1.2N，接近真实力传感器效果。代码已开源。
原文摘要 · Abstract (English)
Many contact-rich manipulation tasks require precise force regulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and adding force/torque sensors to collaborative robot arms is often costly and requires additional hardware design. To overcome these issues, we propose FILIC, a Force-guided Imitation Learning framework with impedance torque control. FILIC integrates a Transformer-based IL policy with an impedance controller in a dual-loop structure, enabling compliant force-informed, force-executed manipulation. For robots without force/torque sensors, we introduce a cost-effective end-effector force estimator using joint torque measurements through analytical Jacobian-based inversion while compensating with model-predicted torques from a digital twin. Experiments show that FILIC significantly outperforms vision-only and joint-torque-based methods, achieving safer, more compliant, and adaptable contact-rich manipulation. The source code is available at https://github.com/OpenGHz/mujoco-wrench-estimator.git.
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