Uncertainty-Gated Exploration Noise Suppresses Task Collapse in Online RL Fine-Tuning of a Flow-Matching Vision-Language-Action Policy
Solving the catastrophic forgetting problem in robotic online reinforcement learning via uncertainty-gated noise.
Online reinforcement learning fine-tuning of pretrained flow-matching vision-language-action (VLA) policies promises robots that keep learning after deployment, but continued updates often destroy competence on individual tasks while the aggregate still looks healthy. We study th...
ArXiv CS.RO
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This research addresses the critical challenge of catastrophic forgetting in deployed robotic systems by introducing a mechanism that maintains task performance during continuous learning. By stabilizing policy updates through uncertainty-gated exploration, it enables more reliable and autonomous robot adaptation in complex, real-world environments.
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