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This paper introduces HERO, a self-improving hierarchical embodied agent designed to enable robots to autonomously evolve their manipulation capabilities without any human demonstrations. By integrating heuristic reasoning, exemplar reuse, and reflexive execution, HERO allows robots to bootstrap their manipulation experiences and dynamically consolidate these into efficient visuomotor policies. Experimental results show that HERO significantly minimizes the need for human intervention while achieving robust performance across a variety of manipulation tasks, highlighting its potential for advancing autonomous robotic systems.
HERO enables robots to autonomously evolve manipulation skills from scratch, drastically reducing reliance on human demonstrations.
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.
