SCORE constrains sim RL to the support of a real-data policy via flow steering, raising average success on eight dexterous tasks from 37.8% to 89.9%.
Simtoolreal: An object-centric policy for zero-shot dexterous tool manipulation
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.RO 5years
2026 5representative citing papers
Task-agnostic RL play pretraining on diverse objects yields a reusable dexterous prior that makes sparse-reward assembly learning ~33× more sample-efficient and enables zero-shot sim-to-real transfer on tight insertions and multi-part tasks.
Video2Sim2Real turns a single human video into a deployable robot manipulation skill by reconstructing a digital twin, anchoring motions to object-centric simulator configurations, and bridging sim-to-real gaps with imitation learning and residual RL.
LUCID learns embodiment-agnostic intent models from unstructured human videos to train dexterous robot policies in simulation, enabling zero-shot transfer on real-world tasks like stirring and wiping.
HandelBot refines simulation policies via physical rollouts and residual RL to achieve precise bimanual piano playing, outperforming direct sim transfer by 1.8x with only 30 minutes of real data across five songs.
citing papers explorer
-
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
SCORE constrains sim RL to the support of a real-data policy via flow steering, raising average success on eight dexterous tasks from 37.8% to 89.9%.
-
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Task-agnostic RL play pretraining on diverse objects yields a reusable dexterous prior that makes sparse-reward assembly learning ~33× more sample-efficient and enables zero-shot sim-to-real transfer on tight insertions and multi-part tasks.
-
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Video2Sim2Real turns a single human video into a deployable robot manipulation skill by reconstructing a digital twin, anchoring motions to object-centric simulator configurations, and bridging sim-to-real gaps with imitation learning and residual RL.
-
LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
LUCID learns embodiment-agnostic intent models from unstructured human videos to train dexterous robot policies in simulation, enabling zero-shot transfer on real-world tasks like stirring and wiping.
-
HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
HandelBot refines simulation policies via physical rollouts and residual RL to achieve precise bimanual piano playing, outperforming direct sim transfer by 1.8x with only 30 minutes of real data across five songs.