RECALL introduces uncertainty-guided active data collection for continual fine-tuning of VLAs, showing efficiency gains over passive imitation but requiring replay or regularization to mitigate catastrophic forgetting.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
RECALL introduces uncertainty-guided active data collection for continual fine-tuning of VLAs, showing efficiency gains over passive imitation but requiring replay or regularization to mitigate catastrophic forgetting.