Noise Space Learning for VLA Adaptation
Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints.
ProofPaper ↗
Key points
- Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed.
- Building on prior noise-steering methods, we formulate NSL as an interface for VLA adaptation that accommodates different learning algorithms and instantiate it with PPO, SAC, and AWR.
- Theoretically, we provide an information-theoretic analysis of how NSL enables efficient adaptation.
- Empirically, we study these NSL algorithms on LIBERO, RoboTwin, and four real-robot manipulation tasks under interaction, computation, and execution constraints.
Sources (1)
- [1]Noise Space Learning for VLA AdaptationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 05:58 PM
Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints.
Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed.
Extractive summary: sentences quoted from the sources.
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