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Paper TuP201.3

Sattar, Junaed (McGill University), Dudek, Gregory (McGill University)

Towards Quantitative Modeling of Task Confirmations in Human-Robot Dialog

Scheduled for presentation during the Regular Sessions "Human and Multi-Robot Interaction" (TuP201), Tuesday, May 10, 2011, 15:55−16:10, Room 3B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Cognitive Human-Robot Interaction, Physical Human-Robot Interaction, Autonomous Agents

Abstract

We present a technique for robust human-robot interaction taking into consideration uncertainty in input and task execution costs incurred by the robot. Specifically, this research aims to quantitatively model confirmation feedback, as required by a robot while communicating with a human operator to perform a particular task. Our goal is to model human-robot interaction from the perspective of risk minimization, taking into account errors in communication, “risk” involved in performing the required task, and task execution costs. Given an input modality with non-trivial uncertainty, we calculate the cost associated with performing the task specified by the user, and if deemed necessary, ask the user for confirmation. The estimated task cost and the uncertainty measure are given as input to a Decision Function, the output of which is then used to decide whether to execute the task, or request clarification from the user. In cases where the cost or uncertainty (or both) is estimated to be exceedingly high by the system, task execution is deferred until a significant reduction in the output of the Decision Function is achieved. We test our system through human–interface experiments, based on a framework custom–designed for our family of amphibious robots, and demonstrate the utility of the framework in the presence of large task costs and uncertainties. We also present qualitative results of our algorithm from field trials of our robots

 

 

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