TY - GEN
T1 - Modeling user's driving-characteristics in a steering task to customize a virtual fixture based on task-performance
AU - Yoon, Han U.
AU - Wang, Ranxiao F.
AU - Hutchinson, Seth A.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/22
Y1 - 2014/9/22
N2 - This paper presents an approach for modeling user's driving-characteristics in a steering task, and determining the parameters of a virtual fixture to assist the user-control on the basis of his/her task-performances. First, we briefly introduce our assistive human-robot interaction (HRI) interface and a virtual fixture as backgrounds related to this research. The designed HRI interface provides assistance by actively constraining the user-control with a virtual fixture. Second, we discuss a way to model a user's driving-characteristics in a steering task. In modeling the driving-characteristics, we use techniques from inverse optimal control (IOC), where known basis functions (speed, steering, and proximities to inner/outer road boundary) are employed to design a cost function. Third, we describe the experimental setup and procedures to obtain user-demonstrated data from human subjects. Utilizing the obtained data sets, we infer the unknown parameter vector by solving inverse optimal control. Afterward, the user's driving-characteristics are expressed in terms of the balances of the inferred parameters, allowing us to find a relationship between the modeled driving-characteristics and task-completion time. Finally, we present a method to set a virtual fixture for a newly given task by predicting the user's task-performances.
AB - This paper presents an approach for modeling user's driving-characteristics in a steering task, and determining the parameters of a virtual fixture to assist the user-control on the basis of his/her task-performances. First, we briefly introduce our assistive human-robot interaction (HRI) interface and a virtual fixture as backgrounds related to this research. The designed HRI interface provides assistance by actively constraining the user-control with a virtual fixture. Second, we discuss a way to model a user's driving-characteristics in a steering task. In modeling the driving-characteristics, we use techniques from inverse optimal control (IOC), where known basis functions (speed, steering, and proximities to inner/outer road boundary) are employed to design a cost function. Third, we describe the experimental setup and procedures to obtain user-demonstrated data from human subjects. Utilizing the obtained data sets, we infer the unknown parameter vector by solving inverse optimal control. Afterward, the user's driving-characteristics are expressed in terms of the balances of the inferred parameters, allowing us to find a relationship between the modeled driving-characteristics and task-completion time. Finally, we present a method to set a virtual fixture for a newly given task by predicting the user's task-performances.
UR - http://www.scopus.com/inward/record.url?scp=84929152248&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929152248&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6906920
DO - 10.1109/ICRA.2014.6906920
M3 - Conference contribution
AN - SCOPUS:84929152248
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 625
EP - 630
BT - Proceedings - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Y2 - 31 May 2014 through 7 June 2014
ER -