让四足机器人像弹簧一样灵活应对外力，提升抗冲击与操控能力
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
- 用强化学习模仿弹簧阻尼系统，实现力适应控制
- 模拟中抗200牛秒冲量，碰撞冲击减少80%
- 可实现在真实机器人上拖拽自重2/3的重物
强化学习在四足机器人控制中取得显著进展，实现了多样地形行走与复杂人机交互能力。然而，常用的位移或速度跟踪目标对机器人所受力不敏感，导致行为僵硬且存在安全隐患，尤其在强力交互时表现不佳。为此，我们提出「力适应控制通过阻抗参考追踪」（FACET）。受阻抗控制启发，我们使用强化学习训练控制策略，模仿虚拟质量-弹簧-阻尼系统，通过调节虚拟弹簧参数实现对外部力的精细调控。仿真结果表明，该四足机器人对高达200牛秒的冲击具有更强鲁棒性，碰撞冲击降低80%。策略成功部署于物理机器人，展示了良好柔顺性，并可通过主动操控牵引重物至自身重量的2/3。进一步扩展至腿式协同操作机器人与人形机器人，验证了方法在更复杂场景中实现全身柔顺控制的可行性。
原文摘要 · Abstract (English)
Reinforcement learning (RL) has made significant strides in legged robot control, enabling locomotion across diverse terrains and complex loco-manipulation capabilities. However, the commonly used position or velocity tracking-based objectives are agnostic to forces experienced by the robot, leading to stiff and potentially dangerous behaviors and poor control during forceful interactions. To address this limitation, we present \emph{Force-Adaptive Control via Impedance Reference Tracking} (FACET). Inspired by impedance control, we use RL to train a control policy to imitate a virtual mass-spring-damper system, allowing fine-grained control under external forces by manipulating the virtual spring. In simulation, we demonstrate that our quadruped robot achieves improved robustness to large impulses (up to 200 Ns) and exhibits controllable compliance, achieving an 80% reduction in collision impulse. The policy is deployed to a physical robot to showcase both compliance and the ability to engage with large forces by kinesthetic control and pulling payloads up to 2/3 of its weight. Further extension to a legged loco-manipulator and a humanoid shows the applicability of our method to more complex settings to enable whole-body compliance control. Project Website: https://facet.pages.dev/
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