Control Strategies for Embodied Humanoid Robots: Reinforcement Learning vs. Multi-Contact Planning

As the application demand for humanoid robots in complex and unstructured environments increases, how to balance adaptability and stability in control strategies has become increasingly critical. This paper compares and analyzes two typical humanoid robot control methods: a reinforcement learning-based controller on the Digit V3 platform and a multi-contact planning and control (MCPC) framework on the COMAN+ platform. This work addresses a gap in the literature that lacks a comparative perspective on cross-method and cross-platform practical verification. It first introduces the design concept and training mechanism of the reinforcement learning controller, which removes reliance on gait clocks and achieves the natural switching between standing and walking under external disturbances. Then it analyzes the MCPC framework, which combines posture sampling, nonlinear programming (NLP) trajectory optimization, and torque-level balance control to support the robot to stably perform complex multi-contact tasks. Experimental results show that the reinforcement learning controller exhibits excellent robustness in disturbance response and command switching, while the MCPC method shows higher accuracy and repeatability in structured tasks. The results of this study show that reinforcement learning is suitable for dealing with scenarios with strong dynamic adaptability, while planning control emphasizes interpretability and physical feasibility. The comparative analysis presented in this article provides a reference for understanding the trade-offs in humanoid robot control strategies and also offers guidance for truly realizing embodied intelligence in humanoid robots in the future.