WorldSample: Closed-loop Real-robot RL with World Modelling
Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations. However, deploying RL on real robots remains constrained by high interaction costs, since each physical rollout is costly and reflects only one realized action-outcome path. To address this challenge, we propose WorldSample, a physically grounded data augmentation framework for real-robot RL that closes a real-synthetic loop between physical rollouts, world-model generation, and policy improvement. Grounded on real rollouts, WorldSample generates high-fidelity synthetic tran...
| Authors | Yuquan Xue, Le Xu, Zeyi Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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