Robot Learning

Continual Learning

Continual learning is the ability of a model to acquire new tasks or adapt to new conditions sequentially without catastrophically forgetting previously learned capabilities. Techniques include regularization methods like elastic weight consolidation, replay of stored or generated past experience, and parameter isolation via adapters or expansion. For robots, continual learning spans new skills, new environments, and hardware drift, ideally from the robot's own ongoing deployment experience.

Why it matters for physical AI

Deployed robots encounter novelty daily, and updating fleet policies with fresh experience while preserving validated skills is essential for improving foundation models after they leave the lab.