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Using biomimicry to build robust robots

Sean explains how modelling animal behaviour can help build robust autonomous robots.
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One of the problems we have in robotics research is what happens when we take the robots out of the laboratory environment and into the real world? And at that point, we have a multitude of sensory information that could potentially confuse the robot. One example of this is how does the robot distinguish between signals that it causes itself and signals that are caused by interaction of the robot with the external world? A nice example of this in humans is tickling. Why can’t we tickle ourselves?
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And the issue there is, basically, it’s thought by the neuroscientists that we predict the consequences of our own motor actions and the sensory consequences of those actions, so if you like, the touch that we feel from our own fingers moving. But we can’t predict how the sensations will be caused by someone else tickling us. That’s an unpredictable thing. And so it’s thought that if we put this kind of algorithm into a robot, the robot will be much better able to distinguish between signals that it causes itself, or sensations that it causes itself, and sensations that are caused by the external world. A nice example of this is in our whisking robot.
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So, we set up an experiment where we had our whisking robot use its whiskers to tactilely investigate the world. The idea was that it was supposed to contact an object and then orient, or turn, towards that object. What we found was that worked fine, except on some occasions it would turn and orient to something that was not there, and we called this a phantom orient, or a ghost orient. The way we corrected this was to set up an algorithm in the robot where it can actually predict the sensory consequences of its own actions. So it could predict the sensory signals generated by its own movement of its whiskers.
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And by doing that, it could then distinguish which sensations were caused by its own whisker movements, and which sensations were caused by contact with the external world, because those contacts with the external world were unpredictable.
Natural organisms, through millions of years of natural selection, have evolved to take on the challenges of their environment. These attributes make them adaptable, versatile and robust and it is these design goals that researchers aim to achieve in robotics.
In this video, Dr Sean Anderson explains how the answer to a biological question – why can’t we tickle ourselves? – helped researchers at The University of Sheffield to solve a particular robotic problem.
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