力感知强化学习与混合无传感器力估计用于轮腿式移动操作
Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation
- Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种结合混合无传感器力估计的力感知强化学习方法，用于轮腿式移动操作，实现无末端力传感器下的力引导控制，并通过仿真和硬件实验验证其性能。
AI中文摘要：
力控移动操作需要一个全身策略来协调运动与手臂动作，同时调节末端执行器的交互力。在浮动基座动力学和变化的支撑接触条件下，这具有挑战性，尤其是当末端执行器的力/力矩传感无法用于控制时。本文提出了一种用于轮腿式移动操作的力感知强化学习方法，结合了混合无传感器力估计。所提方法提供了末端执行器力的结构化估计作为显式策略观测，从而在不使用末端执行器力/力矩传感器进行控制的情况下实现力引导的接触行为。力估计通过结合广义动量观测、接触约束力旋量投影和时间残差学习获得：基于模型的组件提取全身扰动的物理结构化部分，而残差网络补偿剩余的运动相关偏差。估计的力被集成到一个模式条件的全身策略中，配有轴式力/位置选择器，允许在一个控制器内实现自由空间运动、纯力调节和混合力/位置控制。仿真结果展示了改进的无传感器力估计和力控制性能。硬件实验进一步通过定量阀门旋转和混合擦拭评估，以及真实轮腿平台上的力引导开门和零力人引导运动，验证了所提出的控制器。
英文摘要：
Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces. This is challenging under floating-base dynamics and changing support contacts, particularly when end-effector force/torque sensing is unavailable for control. This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation. The proposed method provides a structured estimate of the end-effector force as an explicit policy observation, enabling force-guided contact behavior without using an end-effector force/torque sensor for control. The force estimate is obtained by combining generalized momentum observation, contact-constrained wrench projection, and temporal residual learning: the model-based components extract the physically structured part of the whole-body disturbance, while the residual network compensates the remaining motion-dependent bias. The estimated force is integrated into a mode-conditioned whole-body policy with an axis-wise force/position selector, allowing free-space motion, pure force regulation, and hybrid force/position control within one controller. Simulation results demonstrate improved sensorless force estimation and force-control performance. Hardware experiments further validate the proposed controller through quantitative valve-rotation and hybrid wiping evaluations, together with force-guided door opening and zero-force human-guided motion on a real wheeled-legged platform.
