Anticipating Your Robot For The Best Human Robot Interaction


The Human Robot Interaction is a major challenge, but an important one, as we get more robots interacting with us in our homes and they are built with humanoid features. You see, we prefer to share our homes with things that we perceive as biological rather than unfeeling machines if we are going to have more complex helpful relationships than we do with our toaster.

We do have to be careful that we don’t make them too human-like, but don’t quite make it. Then we could end up with a creepy, zombie-like butler that would fit right in the dip in the Uncanny Valley Theory (1). Cuteness has its merits.

When we interact with another person, we anticipate and tend to copy their action as our mirror neurons fire away. That yawning spread like wildfire in that boring class we have all sat through, didn’t it? This interaction is also known as motor resonance (2). We have two things going on. We have a proactive gaze and then automatic imitation. Thus we anticipate and accommodate the motion. The result is that we feel comfortable as long as the action isn’t threatening.

Our robot butler/caregiver shouldn’t be threatening, so we have to avoid the uncanny valley with no zombies making unexpected moves. The key is to get that motor resonance functioning well when we design our humanoid helpers, but the challenge is then to find experimentally what works best with us humans in an everyday environment.

A review of methods for measuring motor resonance with the human robot interaction has been published by Scuitti et al and they show that the behavioral methodologies such as studying predictive gaze and automatic imitation measurements fit the bill much better than PET, fMRI or EEG scans.

If we can get the right human robot interaction, the advent of rent-a-robot stores may not be far away. It could work out better and more economical than have to pay for a place in a care home.

  1. http://en.wikipedia.org/wiki/Uncanny_valley
  2. A.Sciutti, A.Bisio, F.Nori, G.Metta,L.Fadiga, T.Pozzo and G.Sandini, Int. J. Soc. Robot., 223, 4, (2012)., http://www.springerlink.com/content/g82v75n437259607/fulltext.pdf

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