EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents
Ruofei Ju, Xinrui Wang, Xin Ding, Yifan Yang, Hao Wu, Shiqi Jiang, Qianxi Zhang, Hao Wen, Xiangyu Li, Weijun Wang, Kun Li, Yunxin Liu, Haipeng Dai, Wei Wang, Ting Cao

TL;DR
EmbodiSkill is a training-free framework that enables embodied agents to self-evolve skills through reflection and targeted revision, significantly improving task success in diverse environments.
Contribution
It introduces a novel skill-aware reflection method for self-evolving embodied agents without additional training, addressing limitations of existing trajectory-based skill updates.
Findings
EmbodiSkill improves task success rates on ALFWorld and EmbodiedBench.
It enables a frozen Qwen3.5-27B to reach 93.28% success on ALFWorld.
It outperforms GPT-5.2 used as a direct agent by 31.58%.
Abstract
Embodied agents can benefit from skills that guide object search, action execution, and state changes across diverse environments. Since embodied environments vary across layouts, object states, and other execution factors, these skills must self-evolve from trajectories generated during task execution. However, existing skill self-evolution methods are mainly developed in digital environments and often convert trajectories into coarse skill updates. Directly applying this paradigm to embodied settings is problematic, because a failed task execution may reflect not only incorrect skill content, but also an execution lapse in which the agent fails to follow valid guidance. We propose EmbodiSkill, a training-free framework for embodied skill self-evolution through skill-aware reflection and targeted revision. EmbodiSkill interprets each trajectory with respect to the current skill, uses…
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