EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents

Reusable skills are essential for embodied agents because they provide procedural guidance for acting under changing environments. However, embodied skills often express high-level guidance that must be translated into context-dependent actions, creating a gap between skill guidance and action realization. A failed trajectory may therefore arise from either a skill defect or an execution lapse in which valid guidance is present but overlooked. Existing skill self-evolution methods often do not distinguish these causes and may unnecessarily revise guidance that is already correct. We introduce EmbodiSkill, a training-free framework that uses skill-aware reflection to separate skill-changing evidence from execution lapses. It revises the skill body when guidance needs to be added, refined, or corrected, while updating an appendix to emphasize valid but overlooked guidance. Experiments on ALFWorld and EmbodiedBench show consistent improvements over direct-execution, memory-based, and skill-based baselines. On ALFWorld, under a fully local and model-matched setting, EmbodiSkill improves a frozen Qwen3.5-27B executor from 61.19% to 86.57% task success. Using GPT-5.2 only for offline skill evolution further increases task success to 93.28%.