ASPIRE: 面向机器人的智能体技能发现
ASPIRE: Agentic /Skills Discovery for Robotics
AI总结：
提出ASPIRE系统，通过闭环执行、技能库积累和进化搜索，自主编写和优化机器人控制程序，在扰动、多机器人协作和长时域任务上显著超越现有方法，并实现零样本泛化与仿真到现实的技能迁移。
摘要：
传统机器人编程具有挑战性：它需要协调多模态感知、管理物理接触动力学、处理多样配置和执行失败。我们提出ASPIRE（通过迭代机器人探索的智能体技能编程），这是一个持续学习系统，在代码即策略范式中自主编写和优化机器人控制程序，同时将经验累积到可复用的技能库中。ASPIRE发现跨任务、仿真和真实世界设置以及不同实体形态的技能。它在一个开放循环中运行，包含三个组件：（1）闭环机器人执行引擎，暴露细粒度多模态轨迹，实现自主故障诊断、修复合成和验证；（2）持续扩展的技能库，将验证后的修复提炼为可复用、可迁移的知识；（3）进化搜索，生成多样任务序列和控制程序，以超越单轨迹优化。ASPIRE在扰动下的LIBERO-Pro操作任务上比先前方法提升高达77%，在Robosuite双臂交接任务上提升72%，在BEHAVIOR-1K长时域家庭任务上提升32%。其累积的技能库还能实现对未见长时域任务的零样本泛化：在LIBERO-Pro Long上，ASPIRE达到31%的成功率，而先前方法尽管使用测试时推理和重试，成功率仅为4%。最后，仿真发现的技能提供了仿真到现实迁移的初步证据，显著减少了不同实体形态和机器人API下的真实机器人编程工作量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
