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Developing robotic intelligent systems that can adapt quickly to unseen wildsituations is one of the critical challenges in pursuing autonomous robotics.Although some impressive progress has been made in walking stability and skilllearning in the field of legged robots, their ability to fast adaptation isstill inferior to that of animals in nature. Animals are born with massiveskills needed to survive, and can quickly acquire new ones, by composingfundamental skills with limited experience. Inspired by this, we propose anovel framework, named Robot Skill Graph (RSG) for organizing massivefundamental skills of robots and dexterously reusing them for fast adaptation.Bearing a structure similar to the Knowledge Graph (KG), RSG is composed ofmassive dynamic behavioral skills instead of static knowledge in KG and enablesdiscovering implicit relations that exist in be-tween of learning context andacquired skills of robots, serving as a starting point for understanding subtlepatterns existing in robots' skill learning. Extensive experimental resultsdemonstrate that RSG can provide rational skill inference upon new tasks andenvironments and enable quadruped robots to adapt to new scenarios and learnnew skills rapidly.

RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph