Latent RL Priors for Data-Efficient Dexterous Manipulation
Overview
This research introduces Latent RL Prior, a two-stage framework that reuses control knowledge from large-scale reinforcement learning rollouts to enable data-efficient visual policy learning for dexterous manipulation. By distilling privileged information into a flow-matching action decoder during pretraining and adapting it to visual inputs without privileged labels, the method achieves superior performance in simulated tasks like door lifting and bimanual assembly using significantly fewer demonstrations than full-parameter fine-tuning or full-data baselines.
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Environment: simulation
“arXiv:2611.08458v1 Announce Type: new Abstract: Learning dexterous manipulation from limited visual demonstrations is challenging because policies must acquire both perceptual grounding and precise control, while reinforcement learning (RL) in simulation can acquire rich control behaviors from large-scale interaction with privileged information. We introduce \textbf{Latent RL Prior}, a two-stage framework that reuses”
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- arXiv Robotics — research abstractsSignalLatent RL Priors for Data-Efficient Dexterous Manipulation
This research introduces Latent RL Prior, a two-stage framework that reuses control knowledge from large-scale reinforcement learning rollouts to enable data-efficient visual policy learning for dexterous manipulation. By distilling privileged information into a flow-matching action decoder during pretraining and adapting it to visual inputs without privileged labels, the method achieves superior performance in simulated tasks like door lifting and bimanual assembly using significantly fewer demonstrations than full-parameter fine-tuning or full-data baselines.
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