Passively compliant robots are robots with passive compliant parts, for instance springs or soft body parts. They can be cheaper, safer and more versatile than traditional stiff robotics. Since the compliance introduces non-linearities that are not easy to model analytically, we need to monitor the body with sensors and use machine learning to interpret those sensors.
Reservoir computing allows to train non-linear dynamical systems using only simple machine learning techniques. The unit of our reservoir is a population of spiking neurons. During training the desired motor commands are gradually taught to the closed loop system (with gradual FORCE learning), as illustrated in the figure below. The only weights that need to be learned are those to the readouts.
The neurorobotics platform provides a convenient interface between the robot model and a spiking ‘brain’. After training, we have a closed loop system consisting of only the body and its ‘brain’. The body sensors drive the ‘brain’ activity which in turn drives the actuators. The motor commands are ‘embedded’ into the dynamics of this system. The animation below shows the resulting trained closed loop gait controller. Spike trains are shown for all neurons in one population.
If desired, the system can be trained with an extra input to the reservoir (in addition to sensor inputs). This extra input can be coupled with different motor commands (for instance different gaits, or different frequency of the same gait). After training, this extra input can be set by an external actor (be it another ‘brain’ region or just a human) to control the system in real time: