Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
An object-centric residual reinforcement learning framework improves real-world vision-language-action model robustness through simulation-trained corrective policies that transfer…
Hugging Face · Daily Papers
·Kinam Kim, Namiko Saito
·
·▲ 3 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Kinam Kim, Namiko Saito, Heecheol Kim, Katsushi Ikeuchi, Jaegul Choo, Yasuyuki Matsushita
- 3 upvotes da comunidade
- Temas: Vision-Language-Action models, imitation learning, reinforcement learning, sim-to-real dilemma, residual RL, object-centric representation
Resumo
Resumo original (em inglês), extraído do paper:
An object-centric residual reinforcement learning framework improves real-world vision-language-action model robustness through simulation-trained corrective policies that transfer zero-shot despite sim-to-real challenges.Onde ler
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