Abstract
One of the big challenges in robotics is to endow agents with autonomous and adaptive capabilities. With this purpose, we embedded a cerebellum-based control system into a humanoid robot that becomes capable of handling dynamical external and internal complexity. The cerebellum is the area of the brain that coordinates and predicts the body movements throughout the body-environment interactions. Different biologically plausible cerebellar models are available in literature and have been employed for motor learning and control of simplified objects. We built the canonical cerebellar microcircuit by combining machine learning and computational neuroscience techniques. The control system is composed of the adaptive cerebellar module and a classic control method; their combination allows a fast adaptive learning and robust control of the robotic movements when external disturbances appear. The control structure is built offline, but the dynamic parameters are learned during an online-phase training. The aforementioned adaptive control system has been tested in the Neuro-robotics Platform with the virtual humanoid robot iCub. In the experiment, the robot iCub has to balance with the hand a table with a ball running on it. In contrast with previous attempts of solving this task, the proposed neural controller resulted able to quickly adapt when the internal and external conditions change. Our bio-inspired and flexible control architecture can be applied to different robotic configurations without an excessive tuning of the parameters or customization. The cerebellum-based control system is indeed able to deal with changing dynamics and interactions with the environment. Important insights regarding the relationship between the bio-inspired control system functioning and the complexity of the task to be performed are obtained.
Highlights
Controlling a robotic system that operates in an uncertain environment can be a difficult task if the analytical model of the system is not accurate
The proposed control architecture (Figure 1A) is composed of three main building blocks: the robotic plant, which is the physical structure; the motor primitive generator, which is responsible of the trajectory generation; the controller, which elaborates the torque commands to move each motor to the desired set point
Of the feedback controller, but it is intended to correct the eθn angular position error, whereas the PID corrects the eθn angular velocity error. This is solved through the connection inferior olive-deep cerebellar nuclei (IO-Deep Cerebellar Nuclei (DCN)), which conveys information about the angular position error
Summary
Controlling a robotic system that operates in an uncertain environment can be a difficult task if the analytical model of the system is not accurate. The neuroscientific research line has been investigating mainly on the layered structure of the cerebellar circuit proposing several synaptic plasticity models (Luque et al, 2011, 2014, 2016; Casellato et al, 2015; D’Angelo et al, 2016; Antonietti et al, 2017), network models (Chapeau-Blondeau and Chauvet, 1991; Buonomano and Mauk, 1994; Ito, 1997; Mauk and Donegan, 1997; Yamazaki and Tanaka, 2007; Dean et al, 2010), adaptive linear filter model (Fujita, 1982; Barto et al, 1999; Fujiki et al, 2015), and combination of both (Tolu et al, 2012, 2013) These cerebellar-like models were embedded into bio-inspired control architectures to analyze how the cerebellum adjusts the output of the descending motor system of the brain during the generation of movements (Kawato et al, 1987; Ito, 2008), and how it predicts the action, minimizes the sensory discrepancy and cancels the noise (Nowak et al, 2007; Porrill and Dean, 2007). We will discuss the main findings of the study correlating them to previous literature
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