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Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL

This work proposes using sample-based model predictive control (SMPC) in simulation as an automated expert to generate large-scale offline datasets, then training an off-policy reinforcement learning agent with purely sparse task rewards, eliminating the need for manual dense reward design. Combined with a low-level dynamic stability controller, the high-level agent's learned policies can exceed the original optimal control teacher on task objectives. The authors completed sim-to-real deployment on an arm-equipped Spot quadruped and a G1 humanoid; the paper is the accepted version for CoRL 2026.

Part of the event: SMPC Demonstrations with Sparse Reward Training for Robot Locomotion-Manipulation

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