# ASPIRE：通过迭代机器人探索的自主技能编程系统

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-30 08:00
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmr32d601003vsl1d9cjzcgnr
- 原文链接：https://arxiv.org/abs/2607.00272

## AI 摘要

ASPIRE是一个持续学习系统，在代码即策略范式下自主编写和优化机器人控制程序，并累积经验为可复用的技能库。其三个组件为：闭环执行引擎（提供细粒度多模态轨迹，支持故障诊断、修复验证）、持续扩展的技能库（将修复蒸馏为可迁移知识）、进化搜索（生成多样化任务序列与控制程序）。在LIBERO-Pro扰动测试中比先前方法提升77%，Robosuite双臂交接提升72%，BEHAVIOR-1K长周期家务提升32%。其技能库实现零样本泛化：在LIBERO-Pro Long上ASPIRE成功率31%，对比方法仅4%。模拟发现的技能初步验证了仿真到真实迁移，减少了不同机器人与API上的编程工作量。

## 正文

Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning system that autonomously writes and refines robot control programs in a code-as-policy paradigm while compounding experience into a reusable skill library. ASPIRE discovers skills that persist across tasks, simulation and real-world settings, and embodiments. It operates in an open-ended loop with three components: (1) a closed-loop robot execution engine that exposes fine-grained multimodal traces, enabling autonomous failure diagnosis, repair synthesis, and validation; (2) a continually expanding skill library that distills validated fixes into reusable, transferable knowledge; and (3) evolutionary search that generates diverse task sequences and control programs to explore beyond single-trajectory refinement. ASPIRE surpasses prior methods by up to 77% on LIBERO-Pro manipulation under perturbation, 72% on Robosuite bimanual handover, and 32% on BEHAVIOR-1K long-horizon household tasks. Its accumulated library also enables zero-shot generalization to unseen long-horizon tasks: on LIBERO-Pro Long, ASPIRE achieves 31% success versus 4% for prior methods despite their use of test-time reasoning and retries. Finally, simulation-discovered skills provide initial evidence of sim-to-real transfer, substantially reducing real-robot programming effort across different embodiments and robot APIs.