Robust Pedipulation on Quadruped Robots via Gravitational-moment Minimization

This paper presents adaptive locomotion and manipulation (ALaM), a novel approach for quadruped robots that enables robots to pedipulate with any leg as a manipulator while maintaining balance and locomotion with the remaining three legs. Unlike previous methods that only utilize a designated leg as a manipulator, ALaM enables any leg as manipulator based on the task goal position. For this end, we propose a gravitational moment minimization reward for training pedipulation using the centroid of feet as a pivot point. Experimental results demonstrate that ALaM achieves better stability during manipulation, reduces tracking error by approximately 50% compared to state-of-the-art methods, and completes multi-goal tasks more efficiently by dynamically switching manipulation legs.