从白板到涌现能力：通过真实世界无监督质量多样性发现机器人技能
From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity
AI总结：
本文提出 URSA 框架，扩展 QDAC 以无监督方式让四足机器人在仿真和真实世界中发现并复用多样高性能运动技能，且在损伤适应任务上优于基线。
摘要：
自主技能发现旨在使机器人能够在没有显式监督的情况下获得多样行为。由于安全性和数据效率约束，直接在物理硬件上学习此类行为仍然具有挑战性。现有方法（包括 Quality-Diversity Actor-Critic，QDAC）需要人工定义的技能空间和精心调优的启发式方法，限制了真实世界适用性。我们提出 Unsupervised Real-world Skill Acquisition（URSA），它是 QDAC 的扩展，使机器人能够直接在真实世界中自主发现并掌握多样、高性能的技能。我们证明 URSA 在仿真和真实世界中都能在 Unitree A1 四足机器人上成功发现多样的运动技能。我们的方法同时支持启发式驱动的技能发现和完全无监督设置。我们还表明，所学技能库可复用于下游任务，例如真实世界损伤适应；在该任务中，URSA 在 9 个仿真损伤场景中的 5 个以及 5 个真实世界损伤场景中的 3 个上优于所有基线。我们的结果建立了一个用于真实世界机器人学习的新框架，能够以有限的人工干预实现持续技能发现，代表着迈向更自主、更具适应性的机器人系统的重要一步。演示视频可在 https://adaptive-intelligent-robotics.github.io/URSA 获取。
英文摘要：
Autonomous skill discovery aims to enable robots to acquire diverse behaviors without explicit supervision. Learning such behaviors directly on physical hardware remains challenging due to safety and data efficiency constraints. Existing methods, including Quality-Diversity Actor-Critic (QDAC), require manually defined skill spaces and carefully tuned heuristics, limiting real-world applicability. We propose Unsupervised Real-world Skill Acquisition (URSA), an extension of QDAC that enables robots to autonomously discover and master diverse, high-performing skills directly in the real world. We demonstrate that URSA successfully discovers diverse locomotion skills on a Unitree A1 quadruped in both simulation and the real world. Our approach supports both heuristic-driven skill discovery and fully unsupervised settings. We also show that the learned skill repertoire can be reused for downstream tasks such as real-world damage adaptation, where URSA outperforms all baselines in 5 out of 9 simulated and 3 out of 5 real-world damage scenarios. Our results establish a new framework for real-world robot learning that enables continuous skill discovery with limited human intervention, representing a significant step toward more autonomous and adaptable robotic systems. Demonstration videos are available at https://adaptive-intelligent-robotics.github.io/URSA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
