Distill: Uncovering the True Intent behind Human-Robot Communication
For developers of human-robot interaction systems, Distill provides a method to better align robot behavior with user intent, addressing a key bottleneck in intuitive communication.
Distill addresses the challenge of capturing true user intent in human-robot communication by refining task specifications through removal of unnecessary steps, generalization of steps, and relaxation of ordering constraints. In a crowdsourcing study, it effectively elicited and refined user intent from initial specifications.
As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent: natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for human-robot communication interfaces. Given a task specification provided by the user, Distill (1) removes unnecessary steps; (2) generalizes the meaning behind individual steps; and (3) relaxes ordering constraints between steps. We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.