ASPIRE: Agentic /Skills Discovery for Robotics

Key points

The closed-loop robot execution engine exposes multimodal traces for each primitive, including perception overlays, grasp candidates, trajectories, and collision feedback, enabling autonomous failure diagnosis and repair by the agent.
It accumulates validated repair patterns in a skill library and reuses them as in-context guidance on later tasks, improving adaptation speed as the task set grows.
Evolutionary search goes beyond single-trajectory repair by exploring multiple candidate programs in parallel, which further improves success rates.
It achieves a 31 percent zero-shot success rate on LIBERO-Pro Long, compared with 4 percent for the previous method, demonstrating the generalization effect of the growing skill library.
When sim skills are transferred to a real dual-arm robot, inference tokens are reduced by about 10x and success rates also improve.
Performance improves step by step from a macro average of 14 percent without the execution engine to 62 percent with the engine and 72 percent with evolutionary search.

External research summaries. These are not HDATF publications or measured product results.