Toward perceiving robots as humans: Three handshake models face the turing-like handshake test

Guy Avraham, Ilana Nisky, Hugo L. Fernandes, Daniel E. Acuna, Konrad P. Kording, Gerald E. Loeb, Amir Karniel

Research output: Contribution to journalArticlepeer-review


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) 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.

Original languageAmerican English
Article number6185551
Pages (from-to)196-207
Number of pages12
JournalIEEE Transactions on Haptics
Issue number3
StatePublished - 17 Aug 2012


  • Handshake
  • psychophysics
  • sensorimotor control
  • teleoperation
  • turing test

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Science Applications


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