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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 for fast adaptation isstill inferior to that of animals in nature. Animals are born with a massiveset of skills 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 a massive set offundamental 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 between the 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 reliable skill inference upon new tasks andenvironments, and enable quadruped robots to adapt to new scenarios and quicklylearn new skills.

Unlock Reliable Skill Inference for Quadruped Adaptive Behavior by Skill Graph