Got no one to dance with? Not to worry – you might soon be gliding through the moves, thanks to a robotic instructor designed to teach humans how to dance.
The robot’s designers had already created mechanical dance partners that follow a human’s lead, but the new machine gently guides novices through routines while adapting to their skill level.
This is trickier, says Diego Felipe Paez Granados at Tohoku University in Sendai, Japan, who led the research, because the robot must keep students on course without becoming too forceful.
The 1.8-metre-tall robot has wheels, but its upper body moves like that of a human dancer. A force sensor and two laser rangefinders track its student’s movements, which are compared against motion-capture data recorded from professional dancers to judge their performance.
As they progress, the robot gradually reduces the force used to lead them so they become less reliant on its guidance. Its face displays real-time feedback to help pinpoint mistakes, as well as showing them their overall progress to provide encouragement.
In tests with volunteers who had never waltzed before, five out of six improved, according to results to be presented at the International Conference on Robotics and Automation in Singapore later this month. With another group, the robot was not programmed to adapt to students’ progress and four out of six showed no improvement.
Enabling robots to teach humans through physical interaction could have applications beyond dancing, says Paez Granados, from physical rehabilitation to sports training.
“There are special skills and sports where the trainers are not always so open to everyone,” he says. “If you have this system that is good enough to teach you as well as a professional, then it could have a huge market.”
Etienne Burdet, who works on human-machine interaction at Imperial College London, says the underlying approach has already been adopted in robots used in medical rehabilitation – but extending it to complex, full-body interactions such as dancing is an important contribution.
Understanding more free-form physical interaction between humans and machines could be important for many applications, including the handover of controls between human drivers and autonomous cars, he adds.