# ReCo

ReCo couples a response-consistent reinforcement-learning locomotion policy with policy-aware model predictive control (MPC): response shaping trains the policy to respond to commands consistently and repeatably across randomized dynamics, and an identified closed-loop response model then lets MPC jointly plan locomotion commands and arm motion, reducing position and orientation root-mean-square error (RMSE) on the simulation benchmark by 28.7% and 27.4% relative to the best baseline for each metric, with real-world experiments demonstrating onboard continuous legged manipulation and coordinated base and arm motion.

ReCo couples a response-consistent reinforcement-learning locomotion policy with policy-aware model predictive control (MPC): response shaping trains the policy to respond to commands consistently and repeatably across randomized dynamics, and an identified closed-loop response model then lets MPC jointly plan locomotion commands and arm motion, reducing position and orientation root-mean-square error (RMSE) on the simulation benchmark by 28.7% and 27.4% relative to the best baseline for each metric, with real-world experiments demonstrating onboard continuous legged manipulation and coordinated base and arm motion.