Today， we give robots a /skills library that self-evolves and compounds indefinitely！ Introducing ASPIRE： a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots， launch an evolutionary search over control programs， and distill the best know-how into an ever-expanding library.

ASPIRE is a new type of continual learning： "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. "Distributed training" is a panel of agents each practicing a different skill instead of sharded minibatches.

Here's the beauty： ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift （sim looks toyish next to a real camera） and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess， because it doesn't ship pixels or weights across the gap， but ships the know-how. The robot still has to practice in the real world， not zero-shot， but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware， which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning" tokens （yes， tokens are the new unit of *training* compute ；）