为重肢人形机器人设计模型驱动控制框架，提升动态行走与抗扰能力

Humanoid robots often face significant balance issues due to the motion of their heavy limbs. These challenges are particularly pronounced when attempting dynamic motion or operating in environments with irregular terrain. To address this challenge, this manuscript proposes a whole-body control framework for humanoid robots with heavy limbs, using a model-based approach that combines a kino-dynamics planner and a hierarchical optimization problem. The kino-dynamics planner is designed as a model predictive control (MPC) scheme to account for the impact of heavy limbs on mass and inertia distribution. By simplifying the robot's system dynamics and constraints, the planner enables real-time planning of motion and contact forces. The hierarchical optimization problem is formulated using Hierarchical Quadratic Programming (HQP) to minimize limb control errors and ensure compliance with the policy generated by the kino-dynamics planner. Experimental validation of the proposed framework demonstrates its effectiveness. The humanoid robot with heavy limbs controlled by the proposed framework can achieve dynamic walking speeds of up to 1.2~m/s, respond to external disturbances of up to 60~N, and maintain balance on challenging terrains such as uneven surfaces, and outdoor environments.