Perceiving forces, bumps, and touches from proprioceptive expectations

We present a method for enabling an Aldebaran Nao humanoid robot to perceive bumps and touches caused by physical contact forces. Dedicated touch, tactile or force sensors are not used. Instead, our approach involves the robot learning from experience to generate a proprioceptive motor sensory expectation from recent motor position commands. Training involves collecting data from the robot characterised by the absence of the impacts we wish to detect, to establish an expectation of "normal" motor sensory experience. After learning, the perception of any unexpected force is achieved by the comparison of predicted motor sensor values with sensed motor values for each DOF on the robot. We demonstrate our approach allows the robot to reliably detect small (and also large) impacts upon the robot (at each individual joint servo motor) with high, but also varying, degrees of sensitivity for different parts of the body. We discuss current and possible applications for robots that can develop and exploit proprioceptive expectations during physical interaction with the world.