NVIDIA AI Introduces ASPIRE: A Self-Improving Robotics Framework Reaching 31% Zero-Shot on LIBERO-Pro Long Tasks
Traditional robot programming is hard to scale. It requires orchestrating multimodal perception, physical contact dynamics, diverse configurations, and execution failures by hand. Code-as-policy systems let language models compose these into executable robot programs. That makes robot behavior inspectable, editable, and debuggable. But existing robotic coding agents run in naive execution environments. They receive only coarse, task-level feedback. A failed rollout signals that the task fail
ASPIRE is a continual learning system that enables robots to write and refine their own control programs while building a reusable library of validated fixes. Traditional robot programming requires manually handling perception, motion planning, grasping, and failure cases, but code-as-policy systems let language models compose these into executable programs that can be inspected and debugged. ASPIRE improves upon existing coding agents by providing detailed per-primitive feedback about what specifically failed during execution, rather than just coarse task-level signals, and it preserves solutions as transferable skills instead of discarding them after each task. In benchmarks, ASPIRE achieved 72% on LIBERO-Pro overall and notably reached 31% on held-out long-horizon tasks without prior training on them, compared to about 4% for previous methods.
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