The field of human-robot interaction is one that I’ve covered a few times in the past year or so. As man and machine work increasingly alongside one another, it’s a field of growing importance.
Recent research from Ben-Gurion University of the Negev (BGU) is the latest addition to this burgeoning field, and examined the emerging human preferences when engaging with machines.
The research saw a number of participants tested on their preference when working with a robot on a joint movement task. They hope that the work will lead to the creation of an interactive movement protocol for use in rehabilitation scenarios.
Robotic rehab
The team had previously been developing robots to help people perform their rehabilitation exercises at home, and so wanted to test the best way for such machines to secure engagement from users.
“In the future, human beings may increasingly rely on robotic assistance for daily tasks, and our research shows that the type of motions that the robot makes when interacting with humans makes a difference in how satisfied the person is with the interaction,” they explain. “People feel that if robots don’t move like they do, it is unsettling and they will use them less frequently.”
The participants played a leader-follower mirror game with a robotic arm. The game required each player to take terms with the robot in following the joint movements of their partner.
The game revealed three core findings. Firstly, the human players were good at imitating the movements of the robot, and tended to do so obediently, which the team believe is especially useful in healthcare.
“This is a very important aspect to consider, for example, when designing a robotic nurse who assists a surgeon in the operating room,” they explain. “You wouldn’t want a robotic nurse to make sharp “robotic” movements that will affect the way the surgeon moves his or her hands during an operation.”
The second main lesson that emerged was that the human players didn’t display any clear preference for either leading the robot or following it. There seemed a pretty even split between players who preferred one over the other. The researchers believe this highlights the importance of personalizing human-robot interactions and not assuming one size will fit all.
The final lesson to emerge was around the form of movements undertaken by the robot. The human players significantly preferred smooth and familiar movements that are more ‘human’ in motion than the jerky and sharp movements that can appear more ‘robotic’.
“Thus, determining the elements in the interaction that make users more motivated to continue is important in designing future robots that will interact with humans on a daily basis,” the authors conclude.
