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SARAM: Sparse Adapter Allocation with Reuse and Merging for Lifelong Robot Learning
Lifelong robot learning requires acquiring new tasks sequentially without forgetting earlier ones. With large pre-trained vision-language-action (VLA) backbones, adapter-based methods effectively mitigate this challenge. However, their task-specific adapters and selection modules accumulate with the
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