ASPIRE：面向机器人的自主技能发现

摘要

传统机器人编程极具挑战性：它需要协调多模态感知、管理物理接触动力学、处理多样化的配置及执行故障。我们提出ASPIRE（通过迭代机器人探索的自主技能编程），这是一个持续学习系统，能够在“代码即策略”范式下自主编写和优化机器人控制程序，同时将经验累积为可复用的技能库。ASPIRE发现的技能跨任务、仿真与真实环境以及不同机器人形态持续有效。该系统在开放式循环中运行，包含三个组成部分：（1）闭环机器人执行引擎，提供细粒度多模态轨迹，支持自主故障诊断、修复合成和验证；（2）持续扩展的技能库，将经过验证的修复提炼为可复用、可迁移的知识；（3）进化搜索，生成多样化的任务序列和控制程序，以超越单轨迹优化的探索。ASPIRE在受扰动下的LIBERO-Pro操作任务中比以往方法高出77%，在Robosuite双机械臂交接任务中高出72%，在BEHAVIOR-1K长视界家务任务中高出32%。其累积的技能库还能实现对未见过的长视界任务的零样本泛化：在LIBERO-Pro Long上，ASPIRE达到31%的成功率，而此前方法即使采用测试时推理和重试也仅为4%。最后，仿真中发现的技能为仿真到真实迁移提供了初步证据，显著减少了在不同机器人形态和API下的实际机器人编程工作量。

English

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.