Compliant robot control using cerebellar spiking neural networks, a biologically inspired approach
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Show full item recordAuthor
Abadía Tercedor, IgnacioEditorial
Universidad de Granada
Departamento
Universidad de Granada. Programa de Doctorado en Tecnologías de la Información y la ComunicaciónMateria
Robot control Cerebellar Spiking Neural Networks
Date
2022Fecha lectura
2022-10-07Referencia bibliográfica
Abadía Tercedor, Ignacio. Compliant robot control using cerebellar spiking neural networks, a biologically inspired approach. Granada: Universidad de Granada, 2022. [https://hdl.handle.net/10481/77687]
Sponsorship
Tesis Univ. Granada.Abstract
In the last decades, a new robotics paradigm has been introduced due to physical
human-robot interaction (HRI) and the use of collaborative robots (cobots) equipped
with low-power actuators and elastic components. This scenario requires the use of
cobot controllers able to operate in unstructured environments and that do not depend
on the accurate mathematical modeling of the nonlinear dynamics introduced by elastic
elements. Robot behavior in this context is required to emulate the adaptability and
flexibility of human behavior as much as possible.
The cerebellum, pivotal for human motor control, has long been proposed as an
adaptive controller, and its regular neural structure has allowed the development of
computational models which replicate, to some extent, its structural and functional
properties. Here, we propose a cerebellar-based adaptive controller able to provide
torque control of a cobot with nonlinear dynamics. Using spiking neural networks we
replicate the cerebellum neural topology and synaptic plasticity mechanisms. We then
embed the biologically plausible cerebellar network at the core of a cobot control loop.
The spike-processing computational cost of biologically plausible cerebellar models has
prevented their real-world applicability, thus relegating them to mere theoretical or
simulated models. Within this dissertation, we prove the applicability of our
biologically plausible cerebellar controller in real-world control problems. We present a
cerebellar spiking neural network which is large enough to provide the required
resolution for torque control of six degrees of freedom in real-time, and hence can
operate real cobots. The cerebellar controller provides fine accuracy in the execution of
different motor tasks thanks to the deployed cerebellar learning mechanisms. Besides,
the controller is also able to adapt the cobot behavior to unstructured changes directly
affecting the cobot dynamics. Furthermore, the aforementioned cerebellar control
learning mechanisms can also cope with sensorimotor delays affecting the robotcontroller
communication, a well-known source of control loop instability.
Sensorimotor latency is unavoidable in the central nervous system (CNS), however, it
does not jeopardize the stability of motor control thanks to, among others, cerebellar
predictive behavior. We prove the cerebellar controller robust against sensorimotor
delays of different nature, thus applying to robotics another intrinsic feature of the
cerebellum.
In addition to cerebellar control, we expand the biologically inspired approach with
other key elements of the CNS and musculoskeletal system. We present some first
results of adding spinal cord circuits to the cerebellar controller. The spinal cord, using direct muscle feedback to allow fast-stretch reflexes and muscle activity regulation, is
found to improve cerebellar learning and robustness against perturbations. As next step
we will integrate the spinal cord circuits and the cerebellar controller operating the
cobot, for which muscle dynamics will need to be added to the control loop. Here we
present a preliminary approach for the integration of muscle dynamics within the cobot
control loop, which is shown capable of modifying the motion stiffness of the cobot by
changing the cocontraction degree of antagonistic muscle pairs. Different stiffness
profiles would allow the robot behavior to cover different degrees of admittance and
impedance control, of interest to physical HRI as those control modes directly impact
how the robot reacts to external interactions (admittance control performs better in soft
environments, while impedance control favors stiff environments).
For collaborative robotics to succeed, robot performance must emulate the adaptability
and flexibility of human behavior. Hence, the biological substrate behind human
conduct could be used as inspiration to bring robot behavior closer to our inherent
motor capabilities. Human behavior is sustained by both hardware and software: the
biomechanics of the musculoskeletal system together with the control provided by the
CNS allow us to interact with others and the environment. On the hardware side, robot
design is increasingly mimicking the dynamics of living beings. On the software side,
the study and understanding of the different CNS areas and their computational
replication can expand the family of controllers able to provide adaptive, compliant
robot control. Here, we benefit from decades of neuroscience studies about the
cerebellum structure and functioning, and apply those findings to current robotic
challenges.