MorphAct: Robot Policies Morphing with the Worlds They Act In
Progress toward general intelligence in the physical world requires scaling robot learning across diverse environments and interactions.
ProofPaper ↗
Key points
- However, existing robot policies use shared parameters that remain fixed across interaction contexts.
- This motivates MorphAct, a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change.
- Two coupled generators produce context-dependent low-rank updates for the vision-language and action modules.
- Their coupling follows the policy's computational dependency: vision-language updates shape the representations that condition action-side parameter generation.
Sources (2)
- [1]MorphAct: Robot Policies Morphing with the Worlds They Act InarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 11:31 AM
Progress toward general intelligence in the physical world requires scaling robot learning across diverse environments and interactions.
However, existing robot policies use shared parameters that remain fixed across interaction contexts.
- [2]MorphAct: Robot Policies Morphing with the Worlds They Act InHugging Face Daily Papers · Oct 14, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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