AI 中文总结
本研究提出HERO自改进层级具身智能体，通过整合启发式推理等技术，实现机器人从零人类演示自主演进操作能力，减少数据收集人工干预并提升多样化任务操作稳健性。
AI 中文摘要
通用机器人操作要求机器人在开放世界环境中执行多样化任务，同时随时间提升自身技能。尽管机器人操作领域近期取得了进展，但现有系统仍主要以静态方式获取操作技能，即针对特定任务或设置学习能力，而非通过物理交互自适应演进。正如反复练习使人类形成肌肉记忆，高级操作熟练度需要自主能力演进机制，使机器人能逐步将交互经验转化为更有效的操作能力。为此，我们提出HERO，一种自改进的层级具身智能体，可从零人类演示实现自主能力演进。HERO将启发式推理、范例复用与反射执行整合为统一协调框架，使机器人能自主引导操作经验，通过经验迁移快速积累可复用行为，并逐步将重复交互整合为高效的闭环视觉运动策略。通过将自主数据收集与任务执行紧密耦合，HERO根据经验积累的不同阶段和执行需求持续扩展并动态调度操作能力。大量实验表明，HERO大幅减少了机器人数据收集过程中的人工干预，同时在多样化任务中实现了稳健的操作，为自改进机器人系统提供了一条有前景的路径。
英文摘要
General-purpose robotic manipulation requires robots to perform diverse tasks in open-world environments while improving their skills over time. Despite recent progress in robotic manipulation, existing systems still primarily acquire manipulation skills in a static manner, where capabilities are learned for specific tasks or settings rather than adaptively evolving through physical interaction. Resembling how repeated practice enables humans to develop muscle memory, advanced manipulation proficiency requires an autonomous capability evolution mechanism that allows robots to progressively transform interaction experiences into increasingly effective manipulation abilities. To this end, we propose HERO, a self-improving hierarchical embodied agent that enables autonomous capability evolution from zero human demonstrations. HERO organizes heuristic reasoning, exemplar reuse, and reflexive execution into a unified orchestration framework, allowing robots to autonomously bootstrap manipulation experience, rapidly accumulate reusable behaviors through experience transfer, and progressively consolidate recurring interactions into efficient closed-loop visuomotor policies. By tightly coupling autonomous data collection with task execution, HERO continuously expands and dynamically schedules manipulation capabilities according to different stages of experience accumulation and execution requirements. Extensive experiments demonstrate that HERO substantially reduces human intervention during robotic data collection while achieving robust manipulation across diverse tasks, providing a promising path toward self-improving robotic systems.
