We developed an unsupervised approach to learn temporal abstractions of skills incorporating agent-environment interactions. We hope to learn representations of patterns of motion of objects in the environment, or patterns of change of state. Our approach is able to learn semantically meaningful skill segments across robot and human demonstrations, despite being completely unsupervised.

We developed an algorithmic framework to extract different intrinsic features from human demonstrations. We are studying various features, including interactions with objects along the trajectory, analyzing the environment for interactions with the background (e.g., wiping or writing), and classifying the type of motion within a trajectory segment (e.g., shaking, rotating, or transporting).

We presented a method to learn human motions using a Learning-From-Demonstration approach. Using Dynamic Motion Primitives, we were able to teleoperate a Franka Panda Arm using the learned trajectories.