Detecting Contingency for HRI in Open-World Environments

This paper presents a novel algorithm for detecting contingent reactions to robot behavior in noisy real-world environments with naive users. Prior work has established that one way to detect contingency is by calculating a difference metric between sensor data before and after a robot probe of the environment. Our algorithm, CIRCLE (Contingency for Interactive Real-time CLassification of Engagement) provides a new approach to calculating this difference and detecting contingency, improving the running time for the difference calculation from 2.5 seconds to approximately 0.001 seconds on an 1100-sample vector, and effectively enabling real-time detection of contingent events. We show accuracy comparable to the best offline results for detecting contingency in this way (89.5% vs 91% in prior work), and demonstrate the utility of the real-time contingency detection in a field study of a survey-administering robot in a noisy open-world environment with naive users, showing that the robot can decrease the number of requests it makes (from 38 to 13) while more efficiently collecting survey responses (30% response rate rather than 26.3%).