SelfSkill: Self-State Conditioned Skill Discovery for Embodied Agents

Embodied agents have achieved remarkable progress in interacting with the physical world, yet existing evaluations primarily focus on task-level outcomes, which reveal whether an agent succeeds but provide little insight into what capabilities an agent possesses. In this work, we introduce SelfSkill, a framework for discovering skills of embodied agents. The key insight is that the skills are not fixed behavioral patterns, but emerge from how an agent utilizes different environmental factors under its current embodied conditions, which we characterize as self-states. To characterize such skills, SelfSkill constructs a initial behavioral basis space, where each basis represents a potential behavioral influence induced by an environmental factor. Given trajectory observations, SelfSkill infers the activation pattern over these bases under different self-states, producing a skill that captures how an agent generates behaviors under varying self-states. By associating discovered skills with trajectory outcomes, SelfSkill reveals the strengths and limitations of different agents across self-states. Furthermore, the discovered skills provide interpretable guidance for agent enhancement by identifying complementary capabilities among different agents and transferring effective behavioral knowledge. Experiments on ObjectGoal Navigation and Open-Vocabulary Mobile Manipulation demonstrate that SelfSkill offers a new perspective on understanding, evaluating, and improving embodied agents.