Hugging Face Trending Papers
Rollback: Experience Backtracking in World Models for Policy Improvement
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arXiv AI
4d ago
Rollback: Experience Backtracking in World Models for Policy Improvement
The paper introduces Rollback, a method that uses action‑conditioned world models to backtrack to critical decision points, branch into alternative actions, and compare outcomes to attribute success to individual decisions. By turning world models into counterfactual engines, Rollback provides fine‑grained, action‑level supervision without a separate value model and integrates distributed world‑model search with asynchronous real‑robot execution. Experiments on block extraction, ball tossing, and tabletop curling show that Rollback improves success rates by over 10% compared to the strongest baseline and surpasses human performance on two tasks within eight hours of training.
arXiv AI
Jul 3
WorldSample: Closed-loop Real-robot RL with World Modelling
arXiv:2607. 02431v1 Announce Type: cross Abstract: 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.
arXiv AI
Jun 30
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
arXiv:2602. 13977v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots.
arXiv Computer Vision
Aug 26
Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning
arXiv:2608.24885v1 Announce Type: cross Abstract: Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on a...
Hugging Face Trending Papers
Aug 25
Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning
Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on an unverified assumption: generated futures faithful...
arXiv AI
Oct 1
RoboCoach: World Models as Active Coaches for Compositional Robot Skills
RoboCoach introduces a world-model-guided coaching framework that uses imagined failures to direct demonstration requests and expert updates for robot manipulation tasks. The system, called RIDI, runs reusable skill experts within a shared action-conditioned world model and records the first failing subtask to decide which demonstrations to acquire and which adapters to refine. Experiments on simulation suites and real robots show that with only 150 additional subtask demonstrations, success rates increase dramatically, and the coached experts transfer effectively to unseen task compositions.