Towards Quantitative Modeling of Task Confirmations in Human–Robot Dialog
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. 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 in both open– and closed–water environments.
Junaed Sattar Gregory Dudek
School of Computer Science,McGill University,3480 University Street,Montr(e)al,QC,Canada H3A 2A7
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
1957-1963
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）