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1. Learning to Locomote with Artificial Neural-Network and CPG-based Control in a Soft Snake Robot

   https://doi.org/10.1109/IROS45743.2020.9340763  

   Liu, Xuan; Gasoto, Renato; Jiang, Ziyi; Onal, Cagdas; Fu, Jie (October 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   In this paper, we present a new locomotion control method for soft robot snakes. Inspired by biological snakes, our control architecture is composed of two key modules: A reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns. The two modules are interconnected into a closed-loop system: The RL module, analogizing the locomotion region located in the midbrain of vertebrate animals, regulates the input to the CPG system given state feedback from the robot. The output of the CPG system is then translated into pressure inputs to the pneumatic actuators of the soft snake robot. Based on the fact that the oscillation frequency and wave amplitude of the Matsuoka oscillator can be independently controlled under different time scales, we further adapt the option-critic framework to improve the learning performance measured by optimality and data efficiency. The performance of the proposed controller is experimentally validated with both simulated and real soft snake robots. 

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2. Sensitivity to Control Signals in Triphasic Rhythmic Neural Systems: A Comparative Mechanistic Analysis via Infinitesimal Local Timing Response Curves

   https://doi.org/10.1162/neco_a_01586  

   Yu, Zhuojun; Rubin, Jonathan E.; Thomas, Peter J. (May 2023, Neural Computation)

   Abstract Similar activity patterns may arise from model neural networks with distinct coupling properties and individual unit dynamics. These similar patterns may, however, respond differently to parameter variations and specifically to tuning of inputs that represent control signals. In this work, we analyze the responses resulting from modulation of a localized input in each of three classes of model neural networks that have been recognized in the literature for their capacity to produce robust three-phase rhythms: coupled fast-slow oscillators, near-heteroclinic oscillators, and threshold-linear networks. Triphasic rhythms, in which each phase consists of a prolonged activation of a corresponding subgroup of neurons followed by a fast transition to another phase, represent a fundamental activity pattern observed across a range of central pattern generators underlying behaviors critical to survival, including respiration, locomotion, and feeding. To perform our analysis, we extend the recently developed local timing response curve (lTRC), which allows us to characterize the timing effects due to perturbations, and we complement our lTRC approach with model-specific dynamical systems analysis. Interestingly, we observe disparate effects of similar perturbations across distinct model classes. Thus, this work provides an analytical framework for studying control of oscillations in nonlinear dynamical systems and may help guide model selection in future efforts to study systems exhibiting triphasic rhythmic activity. 

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3. Inferring Task-Space Central Pattern Generator Parameters for Closed-loop Control of Underactuated Robots

   https://doi.org/10.1109/ICRA40945.2020.9196957  

   Kent, Nathan D.; Bhirangi, Raunaq M.; Travers, Matthew; Howard, Thomas M. (May 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA))

   The complexity associated with the control of highly-articulated legged robots scales quickly as the number of joints increases. Traditional approaches to the control of these robots are often impractical for many real-time applications. This work thus presents a novel sampling-based planning ap- proach for highly-articulated robots that utilizes a probabilistic graphical model (PGM) to infer in real-time how to optimally modify goal-driven, locomotive behaviors for use in closed-loop control. Locomotive behaviors are quantified in terms of the parameters associated with a network of neural oscillators, or rather a central pattern generator (CPG). For the first time, we show that the PGM can be used to optimally modulate different behaviors in real-time (i.e., to select of optimal choice of parameter values across the CPG model) in response to changes both in the local environment and in the desired control signal. The PGM is trained offline using a library of optimal behaviors that are generated using a gradient-free optimization framework. 

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4. Harmonic Balance Analysis of Lur’e Oscillator Network With Non-Diffusive Weak Coupling

   https://doi.org/10.1109/LCSYS.2022.3232408  

   Lee, Bryan; Iwasaki, Tetsuya (December 2022, IEEE Control Systems Letters)

   The central pattern generator (CPG) is a group of interconnected neurons, existing in biological systems as a control center for oscillatory behaviors. We propose a new approach based on the multivariable harmonic balance to characterize the relationship between the oscillation profile (frequency, amplitude, phase) and interconnections within the CPG, modeled as weakly coupled oscillators. In particular, taking advantage of the weak coupling, we formulate a low-dimensional matrix whose eigenvalue/eigenvector capture the perturbation in the oscillation profile due to the coupling. Then we develop an algorithm to estimate the perturbed oscillation profile of a given CPG, and suggest an optimization to synthesize the interconnections to produce a given oscillation profile. 

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5. Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles

   https://doi.org/10.1088/2057-1976/ace0c9  

   Bomela, Walter; Singhal, Bharat; Li, Jr-Shin (July 2023, Biomedical Physics & Engineering Express)

   Abstract The ability to finely manipulate spatiotemporal patterns displayed in neuronal populations is critical for understanding and influencing brain functions, sleep cycles, and neurological pathologies. However, such control tasks are challenged not only by the immense scale but also by the lack of real-time state measurements of neurons in the population, which deteriorates the control performance. In this paper, we formulate the control of dynamic structures in an ensemble of neuron oscillators as a tracking problem and propose a principled control technique for designing optimal stimuli that produce desired spatiotemporal patterns in a network of interacting neurons without requiring feedback information. We further reveal an interesting presentation of information encoding and processing in a neuron ensemble in terms of its controllability property. The performance of the presented technique in creating complex spatiotemporal spiking patterns is demonstrated on neural populations described by mathematically ideal and biophysical models, including the Kuramoto and Hodgkin-Huxley models, as well as real-time experiments on Wein bridge oscillators. 

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