TY - GEN
T1 - Towards quantitative modeling of task confirmations in human-robot dialog
AU - Sattar, Junaed
AU - Dudek, Gregory
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84870596196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870596196&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2011.5979633
DO - 10.1109/ICRA.2011.5979633
M3 - Conference contribution
AN - SCOPUS:84870596196
SN - 9781612843865
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1957
EP - 1963
BT - 2011 IEEE International Conference on Robotics and Automation, ICRA 2011
T2 - 2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Y2 - 9 May 2011 through 13 May 2011
ER -