AI RESEARCH
Dejavu: Towards Experience Feedback Learning for Embodied Intelligence
arXiv CS.AI
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ArXi:2510.10181v3 Announce Type: replace-cross Embodied agents face a fundamental limitation: once deployed in real-world environments, they cannot easily acquire new knowledge to improve task performance. In this paper, we propose Dejavu, a general post-deployment learning framework that augments a frozen Vision-Language-Action (VLA) policy with retrieved execution memories through an Experience Feedback Network (EFN). EFN identifies contextually relevant prior action experiences and conditions action prediction on the retrieved guidance.