Framing Human-Robot Task Communication as a POMDP
This addresses the challenge of task communication for general-purpose robots in real-world settings, though it is incremental as it builds on existing POMDP frameworks for human-robot interaction.
The paper tackles the problem of enabling non-technical human owners to communicate new tasks to robots through interaction, proposing a POMDP representation to infer task details from unstructured human signals. Results from a user experiment with a virtual robot show that this approach produces robots robust to teacher error, accurately infers tasks, and is perceived as intelligent.
As general purpose robots become more capable, pre-programming of all tasks at the factory will become less practical. We would like for non-technical human owners to be able to communicate, through interaction with their robot, the details of a new task; we call this interaction "task communication". During task communication the robot must infer the details of the task from unstructured human signals and it must choose actions that facilitate this inference. In this paper we propose the use of a partially observable Markov decision process (POMDP) for representing the task communication problem; with the unobservable task details and unobservable intentions of the human teacher captured in the state, with all signals from the human represented as observations, and with the cost function chosen to penalize uncertainty. We work through an example representation of task communication as a POMDP, and present results from a user experiment on an interactive virtual robot, compared with a human controlled virtual robot, for a task involving a single object movement and binary approval input from the teacher. The results suggest that the proposed POMDP representation produces robots that are robust to teacher error, that can accurately infer task details, and that are perceived to be intelligent.