Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents
Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains challenging. More recently, general-purpose multimodal agents have shown great potential for zero-shot robotic task solving. However, they often incur high execution costs by reasoning and exploring the physical world from scratch. To reduce these costs, we introduce RoboSkill, a framework that connects skill acquisition and reuse through an Explore, Execute, Evolve loop. Within this loop, the agent explores to gather task-relevant information, executes tasks while adapting to feedback, and evolves its skill library based on execution records. It...
| Authors | Sicheng Xie, Yitong Chen, Haidong Cao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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