Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
DGX agentarXiv:2606.27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations. Improving these policies with reinforcement learning (RL)