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Reinforcement Learning-Based Multi-Terrain Whole-Body Control for a Quadruped Mobile Manipulator

The paper proposes a reinforcement learning-based whole-body control framework for quadruped loco-manipulation on complex terrain. The framework combines a hybrid task representation with task-prioritized whole-body learning to coordinate locomotion and manipulation while preserving task-consistent end-effector behavior. The authors also developed a lightweight quadruped mobile manipulation platform for validation, and real-world experiments successfully deployed on complex terrains including slopes up to 30 degrees, stairs, and deformable terrain.

Part of the event: Successful real-world validation of whole-body control for a quadrupedal robotic arm on complex terrain

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