用统一模型预测控制实现四足机器人的运动与操作协同
Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
- 直接优化关节力矩，融合完整逆动力学实现全身协调
- 实测80Hz实时性能，可完成推拉重物等复杂任务
- 适合需要高精度操作与稳定移动的机器人研究者
步态-操作任务要求全身运动协调以有效操控物体同时保持行走稳定，对规划与控制提出重大挑战。本文提出一种全身模型预测控制（MPC）框架，通过全阶逆动力学直接优化关节力矩，实现运动与受力规划执行的一体化。该方法能生成符合物理规律的全身行为，充分考虑系统动力学与约束。采用Pinocchio和CasADi开源框架及前沿内点求解器Fatrop实现。在搭载Unitree Z1机械臂的Unitree B2四足机器人上，实测达到80 Hz实时性能。实验验证了需精细控制末端执行器位置与力的复杂交互任务，如拖拽重物、推动箱子、擦拭白板等。
原文摘要 · Abstract (English)
Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both planning and control. In this work, we propose a whole-body model predictive control (MPC) framework that directly optimizes joint torques through full-order inverse dynamics, enabling unified motion and force planning and execution within a single predictive layer. This approach allows emergent, physically consistent whole-body behaviors that account for the system's dynamics and physical constraints. We implement our MPC formulation using open software frameworks (Pinocchio and CasADi), along with the state-of-the-art interior-point solver Fatrop. In real-world experiments on a Unitree B2 quadruped equipped with a Unitree Z1 manipulator arm, our MPC formulation achieves real-time performance at 80 Hz. We demonstrate loco-manipulation tasks that demand fine control over the end-effector's position and force to perform real-world interactions like pulling heavy loads, pushing boxes, and wiping whiteboards.
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