The presence of computation and transmission-variable time delays within a robotic control loop is a major cause of instability, hindering safe human-robot interaction (HRI) under these circumstances. Classical control theory has been adapted to counteract the presence of such variable delays; however, the solutions provided to date cannot cope with HRI robotics inherent features. The highly nonlinear dynamics of HRI cobots (robots intended for human interaction in collaborative tasks), together with the growing use of flexible joints and elastic materials providing passive compliance, prevent traditional control solutions from being applied. Conversely, human motor control natively deals with low power actuators, nonlinear dynamics, and variable transmission time delays. The cerebellum, pivotal to human motor control, is able to predict motor commands by correlating current and past sensorimotor signals, and to ultimately compensate for the existing sensorimotor human delay (tens of milliseconds). This work aims at bridging those inherent features of cerebellar motor control and current robotic challenges-namely, compliant control in the presence of variable sensorimotor delays. We implement a cerebellar-like spiking neural network (SNN) controller that is adaptive, compliant, and robust to variable sensorimotor delays by replicating the cerebellar mechanisms that embrace the presence of biological delays and allow motor learning and adaptation.
Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same benchmark. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.
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