EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents
A new approach to enhancing the capabilities of embodied agents has emerged with the introduction of EmbodiSkill, a framework designed for skill-aware reflection and self-evolution. This innovative methodology addresses the challenges faced by agents operating in varied and dynamic environments, where the need for adaptive skills is paramount.
Embodied agents, often deployed in real-world scenarios, must navigate diverse layouts, object states, and execution factors. To thrive in such settings, these agents require skills that not only guide their actions but also adapt based on experiences accumulated during task execution. However, traditional skill self-evolution methods have largely been restricted to digital environments. These methods typically convert task trajectories into broad skill updates, which may not be effective when applied to embodied contexts.
One of the primary issues with existing approaches is that a failure in task execution can signify multiple underlying problems. It may indicate an incorrect skill representation or highlight an execution lapse where the agent failed to adhere to valid guidance. This complexity necessitates a more sophisticated approach to skill evolution, leading to the development of EmbodiSkill.
Key Features of EmbodiSkill
EmbodiSkill introduces a training-free framework that emphasizes skill-aware reflection and targeted revision. The framework operates through several distinct mechanisms:
- Trajectory Interpretation: Each trajectory generated during task execution is analyzed concerning the current skill set. This allows the agent to understand how well the skills are guiding its actions.
- Skill-Changing Evidence: The framework identifies evidence that suggests a need for skill updates. By focusing on this evidence, EmbodiSkill can effectively revise the skill body to enhance performance.
- Execution-Lapse Preservation: Rather than discarding valid guidance during an execution lapse, EmbodiSkill emphasizes and retains these insights. This approach ensures that the agent learns from both successes and failures, reinforcing effective strategies.
Experimental Validation
To validate the effectiveness of EmbodiSkill, extensive experiments were conducted in two prominent environments: ALFWorld and EmbodiedBench. The results were compelling, demonstrating a clear improvement in the task success rates of embodied agents utilizing the framework.
In particular, on the ALFWorld platform, a frozen Qwen3.5-27B executor achieved an impressive task success rate of 93.28% when employing EmbodiSkill. This marked a significant enhancement compared to a direct agent utilizing GPT-5.2 without the benefit of skill-based guidance, which only achieved a success rate of 61.70%. The 31.58% improvement underscores the potential of skill-aware self-evolution in enabling agents to accumulate reusable procedural knowledge from their experiences.
Conclusion
The introduction of EmbodiSkill represents a significant advancement in the field of embodied agents, providing a robust framework for skill self-evolution. By leveraging skill-aware reflection and targeted revisions, this innovative approach not only enhances task performance but also facilitates the development of adaptive, reusable skills. As embodied agents continue to be integrated into various applications, methodologies like EmbodiSkill will play a crucial role in their evolution and effectiveness.
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