Learning robot motor skills with mixed reality
This addresses the problem of intuitive robot teaching for end-users, but it appears incremental as it builds on existing MR and DMP methods.
The paper tackles the challenge of enabling end-users to teach robots complex motor tasks by proposing a learning framework that integrates motion demonstrations, task constraints, planning representations, and object information into a Dynamic Movement Primitives-based system, hypothesizing that a Mixed Reality interface will make this intuitive and sample-efficient.
Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots. Related works have used MR interfaces to communicate robot intents and beliefs to a co-located human, as well as developed algorithms for taking multi-modal human input and learning complex motor behaviors. Even with these successes, enabling end-users to teach robots complex motor tasks still poses a challenge because end-user communication is highly task dependent and world knowledge is highly varied. We propose a learning framework where end-users teach robots a) motion demonstrations, b) task constraints, c) planning representations, and d) object information, all of which are integrated into a single motor skill learning framework based on Dynamic Movement Primitives (DMPs). We hypothesize that conveying this world knowledge will be intuitive with an MR interface, and that a sample-efficient motor skill learning framework which incorporates varied modalities of world knowledge will enable robots to effectively solve complex tasks.