An Integrated System for Learning Multi-Step Robotic Tasks from Unstructured Demonstrations

We present an integrated system for segmenting demonstrations, recognizing repeated skills, and generalizing multi-step tasks from unstructured demonstrations.  This method combines recent work in Bayesian nonparametric statistics and learning from demonstration with perception using an RGB-D camera to generalize a multi-step task on the PR2 mobile manipulator.  We demonstrate the potential of our framework to learn a large library of skills over time and discuss how it might be improved with additional integration of components such as active learning, interactive feedback from humans, and more advanced perception.