  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.
