Robot Learning

Lifelong Learning

Lifelong learning, also called continual learning, is the ability of a model to acquire new tasks and knowledge sequentially over time without catastrophically forgetting previously learned capabilities. Techniques include regularization methods such as elastic weight consolidation, experience replay from stored or generated data, and modular or adapter-based architectures that isolate task-specific parameters. Benchmarks such as LIBERO evaluate these trade-offs in robot manipulation settings.

Why it matters for physical AI

Deployed robots continually encounter new objects, layouts, and customer requests; models that must be retrained from scratch for every addition cannot scale, so continual adaptation is central to long-lived fleets.