Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations. Improving these policies with reinforcement learning (RL) is an appealing alternative, but this process often requires expensive training in the real world. Performing policy improvement in simulation instead provides a far cheaper alternative, but unconstrained RL in simulation can exploit contact and dynamics mismatches, resulting in unsafe behaviors that do not transfer to hardware. Common forms of regularization can furthermore limit improvement by overconstraining to an imperfect behavior prior. In this work, we propose Support-Constrained Off-Domain REinforcement (SCORE), a real-to-sim-to-rea...
| Authors | Raymond Yu, William Huey, Mustafa Mukadam et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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