Biomimetic and Biohybrid Systems: 11th International Conference, Living Machines 2022, Virtual Event, July 19–22, 2022, Proceedings (Lecture Notes in Artificial Intelligence)

This book constitutes the proceedings of the 11th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2022, held as virtual event, in July 19–22, 2022. The conference was held virtually due to the COVID-19 crisis. The 30 full papers and 8 short papers presented were carefully reviewed and selected from 48 submissions. They deal with research on novel life-like technologies inspired by the scientific investigation of biological systems; biomimetics; and research that seeks to interface biological and artificial systems to create biohybrid systems. Preface Organization Contents Unit Cell Based Artificial Venus Flytrap 1 Introduction 2 Construction of Unit Cell Based AVF 2.1 Unit Cell Design 2.2 Motion and Actuation Force Characterization 3 Results 4 Discussion 4.1 Snapping Motion of UC AVF 5 Conclusion References Ten Years of Living Machines Conferences: Transformers-Based Automated Topic Grouping 1 Introduction 2 Methods 3 Results 3.1 Cluster Backgrounds 3.2 Temporal Evolution 4 Discussions and Conclusion References Multi-material FDM 3D Printed Arm with Integrated Pneumatic Actuator 1 Introduction 2 Materials and Methods 2.1 Multi-Material 3D Printing Procedure and Parameters 2.2 Soft Robotic Arm Design 2.3 Print Results and Lift Capability 3 Conclusion and Outlook References SNS-Toolbox: A Tool for Efficient Simulation of Synthetic Nervous Systems 1 Introduction 2 Neural Models 2.1 Non-spiking Neurons and Synapses 2.2 Spiking Neurons and Synapses 3 Software Design and Workflow 3.1 Design Phase 3.2 Compilation 3.3 Simulation 4 Results 4.1 Backend Simulation Performance 4.2 Backend Variant Performance 4.3 Example Network Design 5 Discussion and Future Work References Scaling a Hippocampus Model with GPU Parallelisation and Test-Driven Refactoring 1 Introduction 2 Hippocampus Review 2.1 Neuroanatomy 2.2 Unitary Coherent Particle Filter Model 3 Experiment Design 4 Methods 4.1 Task Configuration 4.2 TensorFlow 4.3 Formal Refactoring Process 5 Results 5.1 Bottleneck Locations 5.2 Final System Performance 6 Discussion References Application-Oriented Comparison of Two 3D Printing Processes for the Manufacture of Pneumatic Bending Actuators for Bioinspired Macroscopic Soft Gripper Systems 1 Introduction 2 Materials and Methods 2.1 Printing and Materials 2.2 Actuator Design 3 Results 4 Conclusion and Outlook References Integrating Spiking Neural Networks and Deep Learning Algorithms on the Neurorobotics Platform 1 Introduction 1.1 Background 2 Method 2.1 Neurorobotics Platform 2.2 WhiskEye 2.3 Environment 2.4 Brain Model 2.5 Transfer Functions 2.6 Predictive Coding Network 2.7 Spike Analysis 3 Results 3.1 Head Angle Estimated by the SNN Follows Ground Truth 3.2 PCN Prediction Error Increases with Drift 3.3 Ideothetic Information Drives Network in Periods of Darkness 4 Discussion References Quasi-static Modeling of Feeding Behavior in Aplysia Californica 1 Introduction 2 Model 2.1 Odontophore and I2, I3 Muscles 2.2 Assumptions 2.3 Governing Equations 2.4 Reformulation 3 Results and Discussion References Conversion of Elastic Energy Stored in the Legs of a Hexapod Robot into Propulsive Force 1 Introduction 2 Related Works 3 Proposed Method 3.1 D-Shaped Leg 3.2 Optimization of D-Shaped Leg 3.3 Fabrication of the Leg 4 Experiment 4.1 Fabrication of the Prototype 4.2 Control of the Prototype 4.3 Experimental Results 5 Conclusion References The Shaker: A Platform for Active Perturbations in Neuromechanical Studies of Small Animals 1 Introduction 2 The Shaker: A Three Degrees of Freedom Active Perturbation Platform 3 Preliminary and Coming Experiments References The Modelling of Different Dog Breeds on the Basis of a Validated Model 1 Introduction 2 Material and Methods 3 Results and Discussion References Analyzing 3D Limb Kinematics of Drosophila Melanogaster for Robotic Platform Development 1 Introduction 2 Methods 2.1 Data Collection 2.2 Kinematic Analysis 3 Results 4 Discussion References Gut Feelings: Towards Robotic Personality Generation with Microbial Fuel Cells 1 Introduction 2 Background 3 Methods 4 Results and Discussion 5 Conclusion References Load Feedback from a Dynamically Scaled Robotic Model of Carausius Morosus Middle Leg 1 Introduction 2 Methods 2.1 Robotic Leg Construction 2.2 Robot Forward and Inverse Kinematics 2.3 Robotic Control 2.4 Treadmill and Dynamic Scaling 3 Results 4 Discussion 4.1 Comparison to Biomechanics and Neurophysiology 4.2 Application to Robotics References A Computational Approach for Contactless Muscle Force and Strain Estimations in Distributed Actuation Biohybrid Mesh Constructs 1 Introduction 2 Methods 2.1 Contactless Estimation of Distributed Muscle Strain 2.2 Contactless Estimation of Distributed Muscle Force 2.3 Characterization of a Biaxial Stretching Platform Towards In Vitro Validation 2.4 Statistical Analysis of Contactless Force and Strain Estimation Methods 3 Results and Discussion 3.1 Muscle Strain Estimation 3.2 Muscle Force Estimation 3.3 Characterization and Performance of Biaxial Stretching Platform 4 Conclusion References Development and Characterization of a Soft Bending Actuator 1 Introduction 2 Material Characterization 2.1 Electrical Breakdown Strength 2.2 Dielectric Constant 2.3 Density and Viscosity 2.4 Elastic Behavior 3 Bending Actuator 4 Conclusion and Outlook References .26em plus .1em minus .1emEvaluation of Gait Generation in Quadrupedal Legged Locomotion with Changing Anterior/Posterior Extreme Positions 1 Introduction 2 Spinal Cat Model 2.1 Leg Controller 2.2 Trajectory Planning 2.3 Phase Transition from the Stance to the Swing 3 Simulation with the Spinal Cat Model 3.1 Methods 3.2 Color Maps for Evaluations 3.3 Stability Analysis Using Potential Function 3.4 Results 4 Discussions 4.1 Gaits with Forward Movement 4.2 Walk-Trot Transition Mechanism 5 Conclusion References Active Inference for Artificial Touch: A Biologically-Plausible Tactile Control Method 1 Introduction 1.1 Background and Related Work 2 Methodology 2.1 Experimental Setup 2.2 Task Overview 2.3 Active Inference Framework 2.4 Experimental Conditions 3 Results 3.1 Localisation and Error 3.2 Belief Updating 3.3 Behaviour 4 Discussion References SLUGBOT, an Aplysia-Inspired Robotic Grasper for Studying Control 1 Introduction 1.1 Feeding Behavior in Aplysia and Prior Work 2 Methods 2.1 Fabricating McKibben Artificial Muscles 2.2 Fabricating Flat Artificial Muscles 2.3 Odontophore Design and Fabrication 2.4 Robot Assembly 2.5 Robot Control 3 Results 4 Discussion 4.1 Morphological Differences Between SLUGBOT and Aplysia 4.2 Neuronal Controller 5 Conclusion References Robotic Platform for Testing a Simple Stereopsis Network 1 Introduction 2 Methods 2.1 Robot and Cameras 2.2 Neural Network 3 Preliminary Results and Discussion 3.1 Camera Binocular Images 3.2 Future Work References A Scalable Soft Robotic Cellbot 1 Introduction 2 Concept of Robot Movement 3 Simulation Results for 3-Cell Model 4 Forces on n-Cell Model 5 Simulation Results for n-Cell Model 6 Physical Robot Results 7 Conclusions and Future Work References A Real-World Implementation of Neurally-Based Magnetic Reception and Navigation 1 Introduction 2 Methods 2.1 Summary of Earlier Work: Simulation Baseline 2.2 Current Study: Magnetic Coil System, Robotic Rate Table, and Physical Sensing 2.3 Current Study: Experiments 3 Results 4 Discussion and Conclusion 5 Future Work References Design of a Biomolecular Neuristor Circuit for Bioinspired Control 1 Introduction 2 Methods 2.1 Biological Neuron Model 2.2 Neuristor Model 3 Results and Discussion 4 Conclusion References GymSlug: Deep Reinforcement Learning Toward Bio-inspired Control Based on Aplysia californica Feeding 1 Introduction 2 Methods 2.1 Hybrid Biomechanical Model of the Musculature and Boolean Network Model of Known Motor Neurons 2.2 GymSlug Reinforcement Learning Environment 2.3 Learning Model and Training Setup 3 Results 3.1 Deep Reinforcement Learning Achieves Effective Motor Neuron Control on the Hybrid Simulation Environment 3.2 Robustness of Trained Agent Policy 3.3 Egestion Behavior Training 4 Conclusion References A Synthetic Nervous System with Coupled Oscillators Controls Peristaltic Locomotion 1 Introduction 2 Simplified Worm Robot Kinematic Model 3 Methods 3.1 Mathematical Models 3.2 Functional Subnetwork Tuning 3.3 Stability Analysis 4 Results 4.1 SNS Simulation 4.2 Stability 5 Discussion and Future Work References Simple Reactive Head Motion Control Enhances Adaptability to Rough Terrain in Centipede Walking 1 Introduction 2 Model 3 Result 4 Conclusion and Future Work References Surrogate Modeling for Optimizing the Wing Design of a Hawk Moth Inspired Flapping-Wing Micro Air Vehicle 1 Introduction 2 Method 2.1 Wing Geometry Modeling 2.2 Flapping Kinematics 2.3 Computational Aerodynamic Simulations 3 Results and Discussion References A Novel Multi-vision Sensor Dataset for Insect-Inspired Outdoor Autonomous Navigation 1 Introduction 2 The Biological Principles in Insect Navigation 2.1 Visual Perception 2.2 Insect Visual Navigation Models 2.3 Neuromorphic Processing 3 Dataset Design 3.1 Sensors and Data Acquisition 3.2 Data Processing 4 Experimental Study: Evaluating Familiarity-Based Neural Insect Navigation Models 4.1 Neural Familiarity-Based Insect Navigation Models 4.2 Setup 4.3 Results 5 Conclusion References Using DeepLabCut to Predict Locations of Subdermal Landmarks from Video 1 Introduction 2 Methods 2.1 Data Collection 3 Results 3.1 2D Estimation of Skeletal Landmarks 3.2 3D Estimation of Skeletal Landmarks 4 Discussion References Underwater Light Modulators: Iridescent Structures of the Seagrass Posidonia Oceanica 1 Introduction 2 Materials and Methods 2.1 Plant Samples 2.2 Methods 3 Results 3.1 P. oceanica Leaf Images and Leaf Structure-dependent Reflection 3.2 Microscopy Analysis of the Leaf and Possible Photonic Structure 3.3 Comparison with a Photonic Crystal Model 3.4 Angular Resolved Reflectance Spectra 4 Discussion 5 Conclusions References Canonical Motor Microcircuit for Control of a Rat Hindlimb 1 Introduction 2 Methods 2.1 Modeling 2.2 Tuning 3 Results 4 Discussion References Direct Assembly and Tuning of Dynamical Neural Networks for Kinematics 1 Introduction 2 Methods 2.1 Neuron Model 2.2 Sensory Model 2.3 Tuning Parameters Within the Model 3 Results 4 Discussion References Homeostatic and Allostatic Principles for Behavioral Regulation in Desert Reptiles: A Robotic Evaluation 1 Introduction 2 Background 3 Proposed System 4 Method 5 Results 6 Conclusion and Discussion References Cognitive Architecture as a Service: Scaffolded Integration of Heterogeneous Models Through Event Streams 1 Introduction 2 The Architecture 3 Results 4 Conclusions 5 Future Work References A Synthetic Nervous System Controls a Biomechanical Model of Aplysia Feeding 1 Introduction 2 Methods 2.1 Biomechanical Model 2.2 Synthetic Nervous System 2.3 Neural Control Circuitry 2.4 Model Implementation 3 Results 3.1 Multifunctional Feeding Control 3.2 Comparison of Simulated Ingestive Behaviors with Animal Data 4 Conclusions and Future Directions References Animal Acceptance of an Autonomous Pasture Sanitation Robot 1 Introduction 1.1 Robot Platform 1.2 Animal Testing 2 Methods 2.1 Animal Testing 3 Results 3.1 Animal Testing 4 Conclusions References A Functional Subnetwork Approach to Multistate Central Pattern Generator Phase Difference Control 1 Introduction 1.1 Our Contribution 2 Background 2.1 Central Pattern Generators (CPGs) 2.2 Functional Subnetwork Approach (FSA) 3 Methodology 3.1 Designing Driven Multistate CPGs 3.2 Estimating Phase Lead/Lag 3.3 Eliminating Phase Error 4 Results 5 Discussion 6 Conclusions References Time-Evolution Characterization of Behavior Class Prototypes 1 Introduction 2 Methods 2.1 Experimental Setup and Data Extraction 2.2 Generating Prototypes 3 Results 3.1 Ethograms and Captured Behavior Transitions 3.2 Hit Probabilities and Absorption Times 3.3 Simulations for Real Data and Prototypes 4 Discussion References Correction to: Integrating Spiking Neural Networks and Deep Learning Algorithms on the Neurorobotics Platform Correction to: Chapter “Integrating Spiking Neural Networks and Deep Learning Algorithms on the Neurorobotics Platform” in: A. Hunt et al. (Eds.): Biomimetic and Biohybrid Systems, LNAI 13548, https://doi.org/10.1007/978-3-031-20470-8_7 Author Index