AI RESEARCH
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
arXiv CS.LG
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ArXi:2601.09512v2 Announce Type: replace-cross To teach robots complex manipulation tasks, a common approach is to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired.