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Issue No. 03 - Third Quarter (2012 vol. 5)
ISSN: 1939-1412
pp: 196-207
Amir Karniel , Ben-Gurion University of the Negev, Beer-Sheva
Gerald E. Loeb , University of Southern California, Los Angeles
Konrad P. Kording , Northwestern University Rehabilitation Institute of Chicago, Chicago
Daniel E. Acuna , Northwestern University Rehabilitation Institute of Chicago, Chicago
Hugo L. Fernandes , Northwestern University Rehabilitation Institute of Chicago, Chicago
Ilana Nisky , Stanford University, Stanford
Guy Avraham , Ben-Gurion University of the Negev, Beer-Sheva
ABSTRACT
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.
INDEX TERMS
Humans, Muscles, Force, Computational modeling, Haptic interfaces, Robot sensing systems, Noise, turing test., Handshake, sensorimotor control, psychophysics, teleoperation
CITATION
Amir Karniel, Gerald E. Loeb, Konrad P. Kording, Daniel E. Acuna, Hugo L. Fernandes, Ilana Nisky, Guy Avraham, "Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test", IEEE Transactions on Haptics, vol. 5, no. , pp. 196-207, Third Quarter 2012, doi:10.1109/TOH.2012.16
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