Building plannable representations with mixed reality
We propose Action-Oriented Semantic Maps (AOSMs), a representation that enables a robot to acquire object manipulation behaviors and semantic information about the environment from a human teacher with a Mixed Reality Head-Mounted Display (MR-HMD). AOSMs are a representation that captures both: a) high-level object manipulation actions in an object class's local frame, and b) semantic representations of objects in the robot's global map that are grounded for navigation. Humans can use a MR-HMD to teach the agent the information necessary for planning object manipulation and navigation actions by interacting with virtual 3D meshes overlaid on the physical workspace. We demonstrate that our system enables users to quickly and accurately teach a robot the knowledge required to autonomously plan and execute three household tasks: picking up a bottle and throwing it in the trash, closing a sink faucet, and flipping a light switch off.
