Imagine walking on a flat surface with your eyes blinded. If the slope below your feet changes, you’ll most likely change your posture to keep moving. To explain this, an idea from the 1950s says that we can predict the sensation that will be produced by a motor command sent by our central nervous system. We can therefore tell apart sensations that are due to our own motion and sensations due to external stimuli. When the expected sensation doesn’t match the sensory input, we change our behavior to compensate.
In work by Schröder-Schetelig et al., a robotic walker uses this idea to stay on its two feet. More precisely, the robot uses a neural network (which is a type of controller) to send commands to hip-joint and knee-joint motors such that the robot is able to walk on flat terrain. These motor commands are then copied (efference copy) and fed to a second neural network that captures the internal model of the robot. This model predicts the acceleration the robot should feel given its motor command and current state. If the acceleration is larger than expected, the robot is probably going downhill and should lean back to slow down. Likewise, if the acceleration is lower, the robot is going uphill and should lean forward. Leaning backward and forward is performed by moving a mass that represents the upper body of the robot and is controlled by a third neural network that takes as an input the robot’s predicted acceleration and the measured acceleration given by an accelerometer.
Experiments shown in the video below were conducted on Runbot, a 23cm bipedal robot that is physically constrained to a circular path of 1m radius and can not perform sideway movements. Results show the robot successfully climbing a changing slope.
In the future, Schröder-Schetelig et al. hope to refine the internal model of Runbot, make it climb even steeper slopes and adapt to new and unforeseen environments.