Skill learning and inference framework for skilligent robot

To achieve a certain task, a skilligent robot should be able to learn the skills embedded in that task. Furthermore, the robot should be able to infer such skills to handle uncertainties and perturbations, since most robot tasks are usually daily-life tasks that include many unexpected situations. Therefore, we propose a unified skill learning and inference framework. The framework includes six processing modules: 1) a human demonstration process, 2) an autonomous segmentation process, 3) a dynamic movement primitive learning process, 4) a Bayesian network learning process, 5) a motivation graph construction process, and 6) a skill-inferring process. Based on the framework, the robot learns and infers situation-adequate and goal-oriented skills to handle uncertainties and human perturbations. To show the validity of our framework, some experimental results are illustrated using a robot arm that performs a `tea service´ task.