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

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/i7y5r6vpjnntbn8ki5nder56f
- Original: https://arxiv.org/abs/2608.12063

## Summary

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.