In this paper we present a framework to learn skills from human demonstrations in the form of geometric
nullspaces, which can be executed using a robot. We collect data of human demonstrations, fit geometric nullspaces to them, and also infer their corresponding geometric constraint models. These geometric constraints provide a powerful mathematical model as well as an intuitive representation of the skill in terms
of the involved objects. To execute the skill using a robot, we combine this geometric skill description with the robot’s kinematics and other environmental constraints, from which poses can be sampled for the robot’s execution. The result of our framework is a system that takes the human demonstrations as input, learns the underlying skill model, and executes the learnt skill with different robots in different dynamic environments.
We evaluate our approach on a simulated industrial robot, and execute the final task on the iCub humanoid robot.
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Funding Info:
This research is partially supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046).