ASPIRE is the continual-learning system NVIDIA open-sourced with the University of Michigan, UIUC, Berkeley and CMU (paper arXiv:2607.00272, Apache-2.0). No gradient descent: a coding agent repeatedly writes, runs and repairs robot control programs (code-as-policy) inside a closed-loop execution engine, promotes only verified fixes into a reusable skill library, and uses evolutionary search to escape single-trajectory patching. Driven by Claude Opus 5.5 it averages 99% success across all 80 LIBERO-Pro tasks and 100% on representative Robosuite and BEHAVIOR-1K tasks; perturbed manipulation improves by up to 77%, and unseen long-horizon tasks reach 31% zero-shot against a 4% baseline. On hardware, skills discovered in simulation transfer across embodiments to a real dual-arm station, and online learning even teaches the robot to solve a Rubik's Cube.
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