Ravestate: Distributed Composition of a Causal-Specificity-Guided Interaction Policy
This work addresses the need for efficient and explainable interaction policies in human-robot interaction, though it appears incremental as it refines prior rule-based methods.
The paper tackled the problem of designing human-robot interaction policies by introducing the Signal-Rule-Slot framework with a Bayesian measure called Causal Pathway Self-information, resulting in robust contextual behavior demonstrated through user studies in text-, speech-, and vision-based scenarios.
In human-robot interaction policy design, a rule-based method is efficient, explainable, expressive and intuitive. In this paper, we present the Signal-Rule-Slot framework, which refines prior work on rule-based symbol system design and introduces a new, Bayesian notion of interaction rule utility called Causal Pathway Self-information. We offer a rigorous theoretical foundation as well as a rich open-source reference implementation Ravestate, with which we conduct user studies in text-, speech-, and vision-based scenarios. The experiments show robust contextual behaviour of our probabilistically informed rule-based system, paving the way for more effective human-machine interaction.