A Modular Bio-Inspired Architecture for Motor Learning and Control of Robotics Systems

Classical robotic control methods struggle in overcoming the constraints and challenges of modern robotics applications. Nowadays, robots require a high level of flexibility to adaptively work in a wide range of scenarios. Our study proposes robotic control solutions that take inspiration from the vertebrates’ central nervous system (CNS) to endow robots with the necessary adaptive and predictive capabilities. In the thesis, we first shed light on the neural mechanisms employed by the CNS to produce complex motor movements in dynamically challenging conditions. Among all the CNS regions involved in motor control, the research focuses on the cerebellum, a powerful and compact neural circuit well known for its crucial role in adaptive learning and control of complex motor behaviors. Based on the challenges and considerations identified from the review of the literature, we propose distinct biologically inspired architectures for robotic real-time adaptive motor learning and control in unknown and disturbed environments. The cerebellar-like control schemes embed a cerebellar-like simulations model that aims to artificially reproduce the functionality, plastic learning, modularity, and morphology of the cerebellum through the combination of machine learning, artificial neural networks, and computational neuroscience techniques. The cerebellar-like control schemes mimic through engineering techniques the different theories regarding the acquisition and employment of cerebellar internal models for the control of robotic motor behavior in dynamically changing conditions. The research merges ideas proposed by the scientific community in the last decades into a unique system that is suitable for real-time robotic applications and attempts to answer through robotics experiments various scientific assumptions regarding the cerebellar internal models theories. The empirical results show the incredible contribution that a cerebellar-like system can incorporate whether the robotic control architecture is affected by high modeling errors, unobservable and high dimensional state and action spaces, uncertainties, sensor noise, external perturbations, and changes in the dynamics. Even though there are many ongoing discussions regarding how the cerebellum operates, we believe that the extraordinary potential of the cerebellar-like methods can endow robots with the flexibility and dynamism that modern robotic applications require

Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study

Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understood by emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensory-motor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learning leads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space