• Cerebellum
• Central Pattern Generator
• Spiking neural network
• Brain-inspired control architecture

Current robotic control strategies are mainly based on trajectory planning that adjust the movements based on the next desired state. These control policies do not efficiently perform where the dimensionality of the control problem increases or disturbances from the external environment affect the system behaviour. These issues are critical in locomotion tasks and the need for different control methods arises. To make autonomous robots able to move in a real and dynamic environment, controllers inspired by brain mechanisms have been proposed.
This research project focuses on the design and validation of a brain-inspired control architecture for robotic locomotion. The proposed control architecture involves the interaction among different motion controllers whose communication represents a simplified model of the neural locomotion control in vertebrates.
The presented solution combines classical control strategies with reservoir computing and spiking neural networks to obtain a scalable and adaptable controller by taking advantage of the different learning properties of neural networks. The features and the advantages of a central pattern generator (CPG) as a trajectory planner for robotic locomotion are described. We integrated a cerebellar-like neural network in the control loop in order to provide an adaptive contribution at the joint level. The experiments are performed on the Neurorobotic Platform (NRP) which offers a simulation environment where an interface between spiking neural network and simulated or real robots is easily achievable. The experiments on a simulated modular robot (Fable) show improvements on the locomotion performances of the robot in terms of position error, time delay and covered distance, compared to a control strategy where just the CPG network is taken into consideration. In conclusion, we can derive that a bio-inspired control strategies to design new efficient and adaptable robotic locomotion controller is possible.