Latent RL Priors for Data-Efficient Dexterous Manipulation
Latent 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.
The paper proposes a two-stage framework that distills large-scale RL rollouts into a flow-matching action decoder to enable data-efficient visual policy learning for dexterous manipulation, reporting performance gains in simulated tasks with fewer demonstrations compared to baselines.
Source: arXiv Robotics — research abstracts · Read original article ↗
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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
Open source S2
same decoder can further be shared across arms by learning coordinated visual contexts for bimanual manipulation. Across three simulated tasks, our method outperforms full-parameter fine-tuning of $\pi_{0.5}$ and GR00T N1.7 in the low-data regime; on door lifting and bimanual assembly, it surpasses full-data baselines using $10\times$ and $5\times$ fewer demonstrations, respectively. Further analysis shows that dire
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org