不用力传感器也能实现安全柔顺控制，仅靠电机信号即可
Minimalist Compliance Control
- 用电机电流/电压估计外部力，替代力传感器
- 在机械臂、人形机器人上实现稳定柔顺交互
- 适合各种机器人和规划方法，即插即用
柔顺控制对安全物理交互至关重要，但受限于力矩传感器等硬件要求。现有强化学习方法虽试图绕过这些限制，却常面临仿真到现实的差距、缺乏安全保证且系统复杂。本文提出最小化柔顺控制，仅利用现代伺服与准直驱电机中已有的电机电流或电压信号，无需力传感器、电流控制或学习过程。通过执行器信号与雅可比矩阵估算外部作用力，并融入任务空间阻抗控制器，保持足够的力测量精度以实现稳定、灵敏的柔顺控制。该方法不依赖具体机体结构，可与多种高层规划器无缝集成。我们在机械臂、灵巧手及两台人形机器人上验证了该方法在多类高接触任务中的表现，结合视觉-语言模型、模仿学习与基于模型的规划。结果表明，该方法在不同机器人平台与规划范式下均能实现鲁棒、安全、柔顺的交互。
原文摘要 · Abstract (English)
Compliance control is essential for safe physical interaction, yet its adoption is limited by hardware requirements such as force torque sensors. While recent reinforcement learning approaches aim to bypass these constraints, they often suffer from sim-to-real gaps, lack safety guarantees, and add system complexity. We propose Minimalist Compliance Control, which enables compliant behavior using only motor current or voltage signals readily available in modern servos and quasi-direct-drive motors, without force sensors, current control, or learning. External wrenches are estimated from actuator signals and Jacobians and incorporated into a task-space admittance controller, preserving sufficient force measurement accuracy for stable and responsive compliance control. Our method is embodiment-agnostic and plug-and-play with diverse high-level planners. We validate our approach on a robot arm, a dexterous hand, and two humanoid robots across multiple contact-rich tasks, using vision-language models, imitation learning, and model-based planning. The results demonstrate robust, safe, and compliant interaction across embodiments and planning paradigms.
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