Temporal Logic-Based Intent Monitoring for Mobile Robots

We propose a framework that uses temporal logic specifications to predict and monitor the intent of a robotic agent through passive observations of its actions over time. Our approach uses a set of possible hypothesized intents specified as Buchi automata, obtained from translating temporal logic formulae. Based on observing the actions of the robot, we update the probabilities of each hypothesis using Bayes rule. Observations of robot actions provide strong evidence for its "immediate" short-term goals, whereas temporal logic specifications describe behaviors over a "never-ending" infinite time horizon. To bridge this gap, we use a two-level hierarchical monitoring approach. At the lower level, we track the immediate short-term goals of the robot which are modeled as atomic propositions in the temporal logic formalism. We apply our approach to predicting intent of human workers and thus their movements in an indoor space based on the publicly available THOR dataset. We show how our approach correctly labels each agent with their appropriate intents after relatively few observations while predicting their future actions accurately over longer time horizons.