📄 中文摘要
研究提出了一种全身控制器，该控制器结合了基于模型的接纳控制与强化学习（RL）策略，以实现机器人在行走与操控过程中的协调。此方法允许机器人在与环境交互时，超越固定基座的限制。接纳控制器将外部扭矩（例如人类在物理交互中施加的扭矩）映射为期望的末端执行器速度，从而实现顺应性行为。通过联合追踪手臂和腿部控制器的速度，形成一个统一的六自由度力响应机制，确保在接触交互中的安全性与合规性。
安全的全身运动操控通过模型与学习控制的结合
📄 English Summary
Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control
This research proposes a whole-body controller that integrates model-based admittance control for the manipulator arm with a Reinforcement Learning (RL) policy for legged locomotion. This approach enables robots to interact with their environment beyond the constraints of a fixed base during simultaneous locomotion and manipulation. The admittance controller maps external wrenches, such as those applied by a human during physical interaction, into desired end-effector velocities, facilitating compliant behavior. By jointly tracking the velocities of both the arm and leg controllers, a unified 6-DoF force response mechanism is established, ensuring safety and compliance during contact interactions.