Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test

• Published in: IEEE transactions on haptics 2012, Vol.5 (3), p.196-207
• Main Authors: Avraham, G., Nisky, I., Fernandes, H. L., Acuna, D. E., Kording, K. P., Loeb, G. E., Karniel, A.
• Format: Article
• Language: English
• Subjects: Computational modeling; Force; Handshake; Haptic interfaces; Humans; Muscles; Noise; psychophysics; Robot sensing systems; sensorimotor control; teleoperation; turing test
• ISSN: 1939-1412; 2329-4051
• DOI: 10.1109/TOH.2012.16

In the Turing test a computer model is deemed to "think intelligently" if it can generate answers that are indistinguishable from those of a human. We developed an analogous Turing-like handshake test to determine if a machine can produce similarly indistinguishable movements. The test is administered through a telerobotic system in which an interrogator holds a robotic stylus and interacts with another party - artificial or human with varying levels of noise. The interrogator is asked which party seems to be more human. Here, we compare the human-likeness levels of three different models for handshake: (1) Tit-for-Tat model, (2) λ model, and (3) Machine Learning model. The Tit-for-Tat and the Machine Learning models generated handshakes that were perceived as the most human-like among the three models that were tested. Combining the best aspects of each of the three models into a single robotic handshake algorithm might allow us to advance our understanding of the way the nervous system controls sensorimotor interactions and further improve the human-likeness of robotic handshakes.