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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 50%Gradient-based Planning for World Models at Longer Horizons →
- LinkedLinked via arxiv author · 85%Yuquan Xue →
“WorldSample: Closed-loop Real-robot RL with World Modelling”
- LinkedLinked via arxiv author · 85%Le Xu →
“WorldSample: Closed-loop Real-robot RL with World Modelling”
- LinkedLinked via arxiv author · 85%Zeyi Liu →
“WorldSample: Closed-loop Real-robot RL with World Modelling”
- LinkedLinked via arxiv author · 85%Zhenyu Wu →
“WorldSample: Closed-loop Real-robot RL with World Modelling”
- LinkedLinked via arxiv author · 85%Zhengyi Gu →
“WorldSample: Closed-loop Real-robot RL with World Modelling”
- LinkedLinked via arxiv author · 85%Xinyang Song →
“WorldSample: Closed-loop Real-robot RL with World Modelling”
- LinkedLinked via arxiv author · 85%Bofang Jia →
“WorldSample: Closed-loop Real-robot RL with World Modelling”