SLUGBOT, an Aplysia-inspired Robotic Grasper for Studying Control

Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug $\textit{Aplysia californica's}$ feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the $\textit{Aplysia}$ feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot's normalized translational and rotational kinematics of the odontophore followed profiles observed $\textit{in vivo}$ despite morphological differences. This brings $\textit{Aplysia}$-inspired control $\textit{in roboto}$ one step closer to multifunctional neural control schema $\textit{in vivo}$ and $\textit{in silico}$. Future additions may improve SLUGBOT's viability as a neuromechanical research platform.Comment: Submitted and accepted to Living Machines 2022 conferenc

A gathering of minds: expanding understanding of the origins of biological diversity and the evolution of developmental mechanisms

This paper is a short report on the 2012 Society of Integrative and Comparative Biology Annual Meeting. Charleston, South Carolina, USA. 3-7 January 2012 (abstracts freely available at http://www.sicb.org/meetings/2012/)

Neuromechanical Simulation

The importance of the interaction between the body and the brain for the control of behavior has been recognized in recent years with the advent of neuromechanics, a field in which the coupling between neural and biomechanical processes is an explicit focus. A major tool used in neuromechanics is simulation, which connects computational models of neural circuits to models of an animal's body situated in a virtual physical world. This connection closes the feedback loop that links the brain, the body, and the world through sensory stimuli, muscle contractions, and body movement. Neuromechanical simulations enable investigators to explore the dynamical relationships between the brain, the body, and the world in ways that are difficult or impossible through experiment alone. Studies in a variety of animals have permitted the analysis of extremely complex and dynamic neuromechanical systems, they have demonstrated that the nervous system functions synergistically with the mechanical properties of the body, they have examined hypotheses that are difficult to test experimentally, and they have explored the role of sensory feedback in controlling complex mechanical systems with many degrees of freedom. Each of these studies confronts a common set of questions: (i) how to abstract key features of the body, the world and the CNS in a useful model, (ii) how to ground model parameters in experimental reality, (iii) how to optimize the model and identify points of sensitivity and insensitivity, and (iv) how to share neuromechanical models for examination, testing, and extension by others

From Biological Synapses to "Intelligent" Robots

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot contro

Locomotor system simulations and muscle modeling of the stick insect (Carausius morosus)

It is a matter of fact that even so called "primitive species" (like insects) readily outperform any human locomotive invention with respect to agility, adaptability and reliability - to name the least. The work at hand deals with two aspects that contribute to the pre-eminence of biological, terrestrial locomotor systems, namely motion control and muscle properties. In the first part of this work, a new, biologically well-founded approach for the control of articulated legs is presented. This controller, based on the detailed physiological knowledge of the stick insect's (Carausius morosus) leg control, redundantizes complex forward or backward kinematic calculations by dexterous employment of sensory feedback and muscle properties. This section shows that the collection of segmental coordination rules (which have been studied in the stick insect for several decades) is indeed able to generate periodic, robust middle leg stepping movements in a physical simulation of the animal. Furthermore, the controller is capable of handling stepping in the front and hind leg; although for hind leg stepping minor modifications were necessary. The second part of this work is about muscle modeling and it is divided into three chapters. Lynchpin of any motion is the muscle, and nowadays it is well-accepted that muscle properties are complex and highly variable. Hence, no trivial relationship between motor neuron activity and motion can be expected and typically, computer modeling is required to link the two. This part therefore first describes how a model of the stick insect's extensor tibiae muscle can be developed for individual muscles. The approach presented offers a way to measure and model all properties for the generation of a classical Hill-type model, in a single animal. Therefore it was necessary to reduce the number of measurements, stimulations and the overall time span of the experiment to a degree this muscle could take without severe loss in vitality. After this approach has been described, the next section deals with a possible application of individual muscle modeling. The variation of muscle model parameters is investigated for 10 different individuals. The question of parameter independence is addressed, and in fact it could be shown that there is co-variation between two different pairs of parameters. One correlation was found between two parameters modeling passive static force curve, the other between one parameter of the force-length and one of the force-activation curve. Both correlations suggest that the model can be reduced further. In the final section, isometric and isotonic simulations were performed with different model configurations. It is investigated how far averaging parameters of different animals would influence model performance. This is studied by comparing the error produced by four different model configurations, differing in their share of averaged parameters. Compared to a model entirely composed of averaged parameters, performance of the muscle specific model improves by approximately 40%