title: Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test.
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.
authors.forename: G
authors.initials: G
authors.lastname: Avraham
authors.forename: I
authors.initials: I
authors.lastname: Nisky
authors.forename: H L
authors.initials: HL
authors.lastname: Fernandes
authors.forename: D E
authors.initials: DE
authors.lastname: Acuna
authors.forename: K P
authors.initials: KP
authors.lastname: Kording
authors.forename: G E
authors.initials: GE
authors.lastname: Loeb
authors.forename: A
authors.initials: A
authors.lastname: Karniel