Neuropod: A real-time neuromorphic spiking CPG applied to robotics

Gutiérrez-Galán, Daniel; Domínguez-Morales, Juan Pedro; Perez-Peña, Fernando; Jiménez-Fernández, Ángel; Linares-Barranco, A.

doi:10.1016/j.neucom.2019.11.007

Cited by 51 publications

(37 citation statements)

References 25 publications

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Section: Introductionmentioning

confidence: 99%

“…A thorough treatment of sCPG controllers can be found in Gutierrez-Galan et al ( 2020 ) though more focused on engineering solutions than biological. Most notably, Gutierrez-Galan et al ( 2020 ) and Rostro-Gonzalez et al ( 2015 ) have implemented sCPGs capable of online switching between three discrete gaits. These correspond to the three main gaits observed during insect walking—wave gait (Hughes, 1952 ), tetrapod coordination, and tripod coordination (Graham, 1972 ) reflecting slow, medium, and fast walking speeds, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Although some studies using SNNs for robot locomotion have been inspired by CPGs, they mainly focus on the engineering task of getting the robot to walk and less on studying the CPG. As such, Gutierrez-Galan et al ( 2020 ) determine the gait by the network topology, using the spiking neurons to produce a regular frequency which drives the selection of the gait. Rostro-Gonzalez et al ( 2015 ) implement an architecture where a single neuron controls each motor and connections are changed discretely between these neurons to produce the desired behavior.…”

Section: Introductionmentioning

confidence: 99%

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Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

2020

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Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion relates back to biology where it can provide evidence for theories that are currently not testable on live insects.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

2020

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“…Most commonly, building neurorobotic applications involves hand-crafting and tuning SNNs for the task of interest. In Gutierrez-Galan et al ( 2020 ) the authors build an SNN inspired by biology to implement a central-pattern generator that runs on the SpiNNaker neuromorphic board (Furber et al, 2014 ) and drives a hexapod robot to walk, trot, or run. In Kreiser et al ( 2019 ), the authors hand craft an SNN to run on the Loihi to steer a small rover.…”

Section: Introductionmentioning

confidence: 99%

Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics

DeWolf¹,

Jaworski²,

Eliasmith

2020

Front. Neurorobot.

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In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using tools they are already familiar with, such as Keras and Python. We identify four primary challenges in building robust, embedded neurorobotic systems, including: (1) developing infrastructure for interfacing with the environment and sensors; (2) processing task specific sensory signals; (3) generating robust, explainable control signals; and (4) compiling neural networks to run on target hardware. Nengo helps to address these challenges by: (1) providing the NengoInterfaces library, which defines a simple but powerful API for users to interact with simulations and hardware; (2) providing the NengoDL library, which lets users use the Keras and TensorFlow API to develop Nengo models; (3) implementing the Neural Engineering Framework, which provides white-box methods for implementing known functions and circuits; and (4) providing multiple backend libraries, such as NengoLoihi, that enable users to compile the same model to different hardware. We present two examples using Nengo to develop neural networks that run on CPUs and GPUs as well as Intel's neuromorphic chip, Loihi, to demonstrate two variations on this workflow. The first example is an implementation of an end-to-end spiking neural network in Nengo that controls a rover simulated in Mujoco. The network integrates a deep convolutional network that processes visual input from cameras mounted on the rover to track a target, and a control system implementing steering and drive functions in connection weights to guide the rover to the target. The second example uses Nengo as a smaller component in a system that has addressed some but not all of those challenges. Specifically it is used to augment a force-based operational space controller with neural adaptive control to improve performance during a reaching task using a real-world Kinova Jaco 2 robotic arm. The code and implementation details are provided 1 , with the intent of enabling other researchers to build and run their own neurorobotic systems.

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“…Under these rules, the parameters of each joint are adjusted, namely, joint angle, and angular velocity [4]. In addition, the neural network (NN) is employed to deduce the next gait from the joint trajectories and driving torque [5].…”

Section: Introductionmentioning

confidence: 99%

Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

Qiao¹

2020

JESA

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This paper attempts to improve the terrain adaptability of hexapod robot through gait control. Firstly, the multi-leg coupling in the tripodal gait of the hexapod robot was modeled by Hopf oscillator. Then, annular central pattern generator (CPG) was adopted to simulate the leg movements of hexapod robot between signals. Furthermore, a physical prototype was designed for the gait control test on field-programmable gate array (FPGA), and the algorithm of the rhythmic output of the model was programmed in Verilog, a hardware description language. Finally, the effectiveness of our gait control method was verified through the simulation on Xilinx. The results show that the phase difference of the CPG network remained stable; the designed hexapod robot moved at about 5.15cm/s stably in a tripodal gait, and outperformed wheeled and tracked robots in terrain adaptation. The research findings lay a solid basis for the design of all-terrain multi-leg robots.

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Neuropod: A real-time neuromorphic spiking CPG applied to robotics

Cited by 51 publications

References 25 publications

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics

Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

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