In the field of locomotion task of quadruped robots, Blind Policy and Perceptive Policy each have their own advantages and limitations. The Blind Policy relies on preset sensor information and algorithms, suitable for known and structured environments, but it lacks adaptability in complex or unknown environments. The Perceptive Policy uses visual sensors to obtain detailed environmental information, allowing it to adapt to complex terrains, but its effectiveness is limited under occluded conditions, especially when perception fails. Unlike the Blind Policy, the Perceptive Policy is not as robust under these conditions. To address these challenges, we propose a MBC:Multi-Brain collaborative system that incorporates the concepts of Multi-Agent Reinforcement Learning and introduces collaboration between the Blind Policy and the Perceptive Policy. By applying this multi-policy collaborative model to a quadruped robot, the robot can maintain stable locomotion even when the perceptual system is impaired or observational data is incomplete. Our simulations and real-world experiments demonstrate that this system significantly improves the robot's passability and robustness against perception failures in complex environments, validating the effectiveness of multi-policy collaboration in enhancing robotic motion performance.

翻译：在四足机器人运动任务领域，盲策略与感知策略各具优势与局限。盲策略依赖预设的传感器信息与算法，适用于已知结构化环境，但在复杂或未知环境中缺乏适应性。感知策略通过视觉传感器获取详细环境信息，能够适应复杂地形，但其在遮挡条件下（尤其是感知失效时）效果受限，此时其鲁棒性不及盲策略。为应对这些挑战，我们提出MBC：多脑协同系统，该系统融合多智能体强化学习思想，引入盲策略与感知策略的协作机制。通过将此多策略协同模型应用于四足机器人，即使在感知系统受损或观测数据不完整的情况下，机器人仍能维持稳定运动。仿真与实物实验表明，该系统能显著提升机器人在复杂环境中应对感知失效的通过能力与鲁棒性，验证了多策略协同在增强机器人运动性能方面的有效性。