Posts Tagged Ro-Man
Come meet us at Ro-Man 2017, where Dr, Vardit Sarne-Fleischmann and Shanee Honig will present our work on Gesture vocabulary for a person following robot.
Abstract— Robots that are designed to support people in different tasks at home and in public areas need to be able to recognize user’s intentions and operate accordingly. To date, research has been mostly concentrated on developing the technological capabilities of the robot and the mechanism of recognition. Still, little is known about navigational commands that could be intuitively communicated by people in order control a robot’s movement. A two-part exploratory study was conducted in order to evaluate how people naturally guide the motion of a robot and whether an existing gesture vocabulary used for human-human communication can be applied to human-robot interaction. Fourteen participants were first asked to demonstrate ten different navigational commands while interacting with a Pioneer robot using a WoZ technique. In the second part of the study participants were asked to identify eight predefined commands from the U.S. Army vocabulary. Results show that simple commands yielded higher consistency among participants regarding the commands they demonstrated. Also, voice commands were more frequent than using gestures, though a combination of both was sometimes more dominant for certain commands. In the second part, an inconsistency of identification rates for opposite commands was observed. The results of this study could serve as a baseline for future developed commands vocabulary promoting a more natural and intuitive human-robot interaction style.
Two of our works have been accepted as full papers for presentation and publication in the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2016).
“Postures of a Robot Arm – window to robot intentions?” authored by my doctoral student Sridatta Chaterjee and co-authored by my colleagues Drs. Oren Shriki and Idit Shalev.
Abstract— Body language of robot arms, have rarely been explored as a medium of conveying robot intentions. An exploratory study was done focusing on two questions: one, if robot arm postures can convey robot intentions, and two, if participants coming in contact with this robot arm for the first time can associate any meaning to the postures without watching the robot in action, or working with it. Thirty five participants of a wide age range (25-70) took part in this exploratory study. Results show that participants could interpret some postures. Four distinct types of postures have been selected to four separate categories by the majority of participants irrespective of their age. In addition, postures selected in categories like, ‘Robot giving object in a friendly manner’; ‘Robot is saying Hi!’, ‘Robot has been told not to disturb’ show similarity to body language exhibited by humans and animals while communicating such messages.
“The Influence of Following Angle on Performance Metrics of a Human-Following Robot” co-authored by our graduate students Shanee Honig and Dror Katz, and my colleague Prof. Yael Edan.
Abstract— Robots that operate alongside people need to be able to move in socially acceptable ways. As a step toward this goal, we study how and under which circumstances the angle at which a robot follows a person may affect the human experience and robot tracking performance. In this paper, we aimed to assess three following angles (0◦ angle, 30◦ angle, and 60◦ angle) under two conditions: when the robot was carrying a valuable personal item or not. Objective and subjective indicators of the quality of following and participants’ perceptions and preferences were collected. Results indicated that the personal item manipulation increased awareness to the quality of the following and the following angles. Without the manipulation, participants were indifferent to the behavior of the robot. Our following algorithm was successful for tracking at a 0◦ and 30◦ angle, yet it must be improved for wider angles. Further research is required to obtain better understanding of following angle preferences for varying environment and task conditions.
NY, Looking forward to two great presentations!