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ASPIRE

Project

ASPIRE introduces an agentic robot learning framework where robots execute code-based policies, analyze multimodal execution traces, automatically repair failed programs, validate repaired behaviors, and store reusable skills for future tasks. The framework combines iterative execution, LLM-guided program repair, evolutionary search, and a growing skill library to improve long-horizon robot performance and generalization.

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Updated:7/2/2026

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Tags

robot-programmingrobot-learningcode-as-policyllmcontinual-learningagentic-aiskill-library

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Sources

ASPIRE - NVIDIA GEAR Lab
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