Learning Abstractions by Transferring Abstract Policies to Grounded State Spaces

Learning from demonstration is an effective paradigm to teach specific tasks to robots. However, such demonstrations often have to be performed on the robot, which is both time-consuming and often still requires expert knowledge (e.g., kinesthetically controlling the joints). It is often easier to specify tasks at a high level of abstraction, and let the robot figure out the grounding to the robot/agent space. We consider how to learn such a mapping. In particular, we consider the task of learning to navigate on a mobile robot given only an abstraction of the path and potential landmarks. We cast this as a learning problem between abstract and robot (grounded) state spaces and illustrate how this works in several cases. Through these cases, we see that the ``abstract navigation'' task touches on many interesting issues related to abstraction, and suggest avenues for further investigation.