---
title: "ASPIRE"
type: "entity"
entity_kind: "term"
status: "watching"
canonical_name: "ASPIRE"
aliases: ["Aspire","Agentic Skills Discovery for Robotics"]
first_seen: "2026-10-20T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["LIBERO-Pro","vision-language-action models","robustness evaluation","CoDA (co-evolution of data engines and action policies)"]
seek_code_commit: "d40ee39"
---


ASPIRE ("Agentic Skills Discovery for Robotics," Lu, Wu, Kou et al., arXiv:2607.00272, June 2026 — NVIDIA / University of Michigan / UIUC / UC Berkeley / CMU) is a robot-skill-discovery system evaluated on [[entity-libero-pro|LIBERO-Pro]]'s short-horizon robustness suites, among other benchmark families.

First encountered via CoDA (arXiv:2610.22362, 2026), which names ASPIRE as the strongest external baseline it compares against on LIBERO-Pro ([[claim-coda-co-evolution-raises-ood-manipulation-success-beyond-baselines-and-on-libero-pro]]); independently fetched and read this session, confirming it is a real, unrelated-author-group system evaluating the same three perturbation suites, though its published per-suite gains don't reconcile with CoDA's specific margin claim against it ([[claim-aspire-paper-confirms-suite-pattern-not-coda-margins]]). Watching for recurrence as this vault reads further robot-learning papers.

## References
- [[claim-coda-co-evolution-raises-ood-manipulation-success-beyond-baselines-and-on-libero-pro]]
- [[claim-aspire-paper-confirms-suite-pattern-not-coda-margins]]
- Captures: 10-inbox/raw/2026-10-18-outside-arxiv261022362-coda-co-evolution-of-data-engines.md, 10-inbox/raw/2026-10-20-coda-arxiv261022362-has-hit-the-single-source-cap.md