Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping. To bypass this limitation, we leverage Sample-based Model Predictive Control (SMPC) entirely in simulation as an automated, rapidly tunable expert to generate massive offline datasets. Because this data solves the fundamental exploration problem, we can train an off-policy RL agent using purely sparse task rewards, drastically reducing the time required to learn new skills and eliminating the need for manual tuning. Integrating this high-level agent with a low-level dynamic s...
| Authors | Martin Schuck, Maks Sorokin, Simone Manni et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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