Preserving Plasticity in Continual Robot Learning
Published:
Swapping AdamW for a Stiefel-constrained optimizer keeps pixel-based robot learners plastic for 100M frames, and a Gram-preserving variant improves continual fine-tuning of a VLA on a real SO-101 arm from 37% to 62% success.
