Title: Dejavu: Post-Deployment Learning for Embodied Agents via Experience Feedback
Primary area: applications to robotics, autonomy, planning
Keywords: Embodied Intelligence, Vision-Language-Action Policy

Abstract: Embodied agents face a fundamental limitation: once deployed, their policy weights remain frozen, preventing adaptation to new experiences. We introduce Dejavu, a framework for post-deployment learning without parameter updates. At its core, Dejavu employs an Experience Feedback Network (EFN), which augments a frozen vision-language-action policy with retrieved execution memories. At each step, EFN identifies the most contextually successful prior experience, balancing task completion and efficiency, and conditions the action prediction on this retrieved guidance. EFN is trained with reinforcement learning and semantic similarity rewards, ensuring that the predicted action aligns with past successful behaviors under current observations. During deployment, Dejavu continually enriches its memory with new trajectories, enabling the agent to exhibit “learning from experience” despite fixed weights. Experiments across diverse embodied tasks show that Dejavu significantly improves adaptability, robustness, and success rates over frozen baselines. These results highlight a promising path toward embodied agents that continually refine their behavior after deployment.