Planning for Human-Robot Interaction in Socially Situated Tasks

摘要

This article presents the results of a study on the effects of representing time and intention in models of socially situated tasks on the quality of policies for robot behavior. The ability to reason about how others' observable actions relate to their unobservable intentions is an important part of interpreting and responding to social behavior. It is also often necessary to observe the timing of actions in order to disambiguate others' intentions. Therefore, our proposed approach is to model these interactions as time-indexed partially observable Markov decision processes (POMDPs). The intentions of humans are represented as hidden state in the POMDP models, and the time-dependence of actions by both humans and the robot are explicitly modelled. Our hypothesis is that planning for these interactions with a model that represents time dependent action outcomes and uncertainty about others' intentions will achieve better results than simpler models that make fixed assumptions about people's intentionality or abstract away time-dependent effects. A driving interaction governed by social conventions and involving ambiguity in the other driver's intent was used as the scenario with which to test this hypothesis. A robot car controlled by policies from time-dependent POMDP models or by policies from two less expressive model variants performed this interaction in a driving simulator with human drivers. The time-dependent POMDP policies achieved better results than those of the models without explicit time representation or human intention as hidden state, both according to the reward obtained and to people's subjective impressions of how socially appropriate and natural the robot's behavior was. These results demonstrate both the relative superiority of these representation choices and the effectiveness of this approach to planning for socially situated tasks.