NVIDIA open-sources a self-improving robot agent
Why it matters
NVIDIA's GEAR lab (with UMich, UIUC, UC Berkeley and CMU) released ASPIRE v1.0 on GitHub on Oct 16 under Apache 2.0. An AI coding agent writes robot control code, inspects detailed execution traces, diagnoses failures, repairs and re-tests its programs, and saves validated fixes as reusable text skills: training is skill refinement, not gradient descent. On a real two-armed robot with GPT-6 Astra, across three Rubik's Cube episodes (the first unsolved, the next two solved), time per completed face turn fell from about 17 minutes to about 3 and tokens per turn from 11.3M to 2.0M. In simulation with Claude Opus 5.5 it averages 99% success across all 80 LIBERO-Pro tasks; on 10 LIBERO-Pro tasks success rises from 15% (GPT-6 Luna) to 99% (Claude Opus 5.5). Caveats: a symbolic cube solver computes the moves (the agent handles perception, grasping and turning), and NVIDIA says ASPIRE is not yet a fully autonomous real-world learner.
Read the source: NVIDIA GEAR (opens in a new tab)
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