Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive progress has been made in walking stability and skill learning in the field of legged robots, their ability to fast adaptation is still inferior to that of animals in nature. Animals are born with massive skills needed to survive, and can quickly acquire new ones, by composing fundamental skills with limited experience. Inspired by this, we propose a novel framework, named Robot Skill Graph (RSG) for organizing massive fundamental skills of robots and dexterously reusing them for fast adaptation. Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of massive dynamic behavioral skills instead of static knowledge in KG and enables discovering implicit relations that exist in be-tween of learning context and acquired skills of robots, serving as a starting point for understanding subtle patterns existing in robots' skill learning. Extensive experimental results demonstrate that RSG can provide rational skill inference upon new tasks and environments and enable quadruped robots to adapt to new scenarios and learn new skills rapidly.

翻译：发展能够快速适应未知野外环境的机器人智能系统，是追求自主机器人的关键挑战之一。尽管在足式机器人的行走稳定性和技能学习领域已取得了令人瞩目的进展，但其快速适应能力仍逊于自然界中的动物。动物天生具备大量生存所需的技能，并能通过组合基础技能与有限的经验快速习得新技能。受此启发，我们提出一种名为“机器人技能图”（RSG）的新型框架，用于组织机器人的大量基础技能，并灵巧地重用这些技能以实现快速适应。与知识图谱（KG）结构相似，RSG由大量动态行为技能而非KG中的静态知识构成，能够发现学习情境与机器人习得技能之间存在隐含关系，为理解机器人技能学习中的微妙模式奠定基础。大量实验结果表明，RSG能够针对新任务和环境提供合理的技能推理，使四足机器人能够快速适应新场景并学习新技能。