Vision language action (VLA) policies continue to grow in parameter count, making deployment on resource-constrained robot platforms difficult. The central goal is to reduce the number of LLM-side parameters retained in the deployed policy while preserving downstream task performance. Our approach, AdaDE, adapts select...
Mu-Chun Niu, Shuang Chen, Yu-Zhou Wu et al.· 0 citations
A system level acceleration strategy that reduces computation in both perception and action generation and compress diffusion sampling into a compact 2-step schedule through efficiency oriented training while preserving action precision is proposed.
Method, an egocentric world-action simulator that synthesizes controllable, high-quality manipulation videos to expand scarce real-world training data, is presented, demonstrating that the synthesized data substantially improve downstream WAM generalization.
Zexuan Yan, Yuzhou Wu, Yue Ma et al.· arXiv.org· 0 citations
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