Robot Learning

Incremental Learning

Incremental learning is the continual updating of a model as new data arrives, without retraining from scratch and without catastrophically forgetting previously acquired capabilities. Techniques include regularization toward old weights, replay of stored or generated past data, and modular architectures that add capacity per task, all balancing plasticity for new skills against stability of old ones.

Why it matters for physical AI

Deployed fleets encounter new objects, layouts, and tasks continuously; robots that can fold this experience into their policies without regressing existing skills compound in value over time.