Rank-Then-Act: Reward-Free Control from Frame-Order Progress
Rank-Then-Act framework learns control policies from video demonstrations using a vision-language model as an ordinal scorer with correlation-based rewards, enabling stable cross-t…
Hugging Face · Daily Papers
·Yuriy Maksyuta, George Bredis
·
·▲ 6 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Yuriy Maksyuta, George Bredis, Ruslan Rakhimov, Daniil Gavrilov
- 6 upvotes da comunidade
- Temas: Vision-Language Model, Group Relative Policy Optimization, ordinal scorer, Spearman rank correlation, reinforcement learning, policy learning
Resumo
Resumo original (em inglês), extraído do paper:
Rank-Then-Act framework learns control policies from video demonstrations using a vision-language model as an ordinal scorer with correlation-based rewards, enabling stable cross-task transfer without environment rewards.Onde ler
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