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

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7 Author(s)
Avraham, G. ; Dept. of Biomed. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel ; Nisky, I. ; Fernandes, H.L. ; Acuna, D.E.
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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.

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Haptics, IEEE Transactions on  (Volume:5 ,  Issue: 3 )