A UMBC study reveals that classical Indian dance mudras provide robots with more versatile motion patterns than natural human grasps.
Ashwathi Menon, UMBC Indian fusion team co-captain, demos lab technology
Photo Credit: Brad Ziegler/UMBC
Researchers are turning to classical Indian dance to teach robots how to use their hands with greater dexterity. A new study from the University of Maryland, Baltimore County (UMBC) shows that the structured hand-gestures, or mudras, found in traditions such as Bharatanatyam can provide robots with a richer and more adaptable movement “alphabet” than those derived solely from natural human grasps. The findings suggest that artistic movement systems may unlock new pathways for robotic precision.
According to the study, the researchers analysed a set of everyday human grasps and compared them with 30 classical mudras, breaking each into underlying motion patterns known as synergies. While both sets could be reduced to six core synergies, the mudra-based combinations proved far more versatile.
The dance-derived synergies performed better than those produced by routine grasps when tested on reconstructing hand shapes from American Sign Language, suggesting a wider expressive range. Scientists argue that this reflects how classical dance codifies movement in a systematic, “super-human” form, offering robots more nuanced control templates.
The group is currently endeavouring to design these synergies into robotic hands and humanoid systems instructions. The possible uses will be assistive robots that can perform delicate household operations, service robots that may demand expressive motions, and better control of the prosthetic hands.
The strategy can also endorse the rehabilitation devices based on gesture recognition. The researchers are ultimately hoping that with the combination of engineering and the systems of artistic knowledge, one day the robots will be able to handle the finely-tuned movements of the hands, small things and even read the signs with more fluidity and cultural relevance.
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