A neuromorphic control architecture for a biped robot

Folgheraiter, Michele; Keldibek, Amina; Aubakir, Bauyrzhan; Gini, Giuseppina; Franchi, Alessio Mauro; Bana, Matteo

doi:10.1016/j.robot.2019.07.014

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…Accordingly, neuromorphic implementation of IK and PID control was addressed in several studies. For example, Folgheraiter et al ( 2019 ) utilized LIF neurons to implement a learning algorithm for adaptive motion control. Barhen and Gulati ( 1991 ) demonstrated neuromorphic inverse kinematics, concentrating on redundant manipulators, using terminal attractors.…”

Section: Discussionmentioning

confidence: 99%

Neuromorphic NEF-Based Inverse Kinematics and PID Control

et al. 2021

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Neuromorphic implementation of robotic control has been shown to outperform conventional control paradigms in terms of robustness to perturbations and adaptation to varying conditions. Two main ingredients of robotics are inverse kinematic and Proportional–Integral–Derivative (PID) control. Inverse kinematics is used to compute an appropriate state in a robot's configuration space, given a target position in task space. PID control applies responsive correction signals to a robot's actuators, allowing it to reach its target accurately. The Neural Engineering Framework (NEF) offers a theoretical framework for a neuromorphic encoding of mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. In this work, we developed NEF-based neuromorphic algorithms for inverse kinematics and PID control, which we used to manipulate 6 degrees of freedom robotic arm. We used online learning for inverse kinematics and signal integration and differentiation for PID, offering high performing and energy-efficient neuromorphic control. Algorithms were evaluated in simulation as well as on Intel's Loihi neuromorphic hardware.

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Section: Discussionmentioning

confidence: 99%

Neuromorphic NEF-Based Inverse Kinematics and PID Control

et al. 2021

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“…CPG-based control can be an important component of the overall control architecture, in which complex rhythmic patterns need to be generated in a parametric and adaptive way, as, e.g., in robots that change their locomotive behavior when switching between environments (water versus ground) [115]. This is an active area of research both in bioinspired robotics and neuromorphic computing [116]- [119].…”

Section: Closed-loop Control For Roboticsmentioning

confidence: 99%

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

et al. 2021

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Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today's neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based

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“…With recent advances in reservoir computing (Salehinejad et al, 2017 ) and its physical implementations (Tanaka et al, 2018 ), our approach offers an alternative to using external arbitrary time-varying signals to control the dynamics of a recurrent network. Our model may also be extended to neuromorphic hardware, where it may benefit chaotic networks employed in robotic motor control (Folgheraiter et al, 2019 ). Finally, our model is, to our knowledge, the first to produce temporal rescaling of natural speech, with implications extending to conversational agents, brain-computer interfaces, and speech synthesis.…”

Section: Discussionmentioning

confidence: 99%

Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations

Vincent-Lamarre

Calderini

Thivierge

2020

Front. Comput. Neurosci.

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Many cognitive and behavioral tasks-such as interval timing, spatial navigation, motor control, and speech-require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.

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A neuromorphic control architecture for a biped robot

Cited by 8 publications

References 34 publications

Neuromorphic NEF-Based Inverse Kinematics and PID Control

Neuromorphic NEF-Based Inverse Kinematics and PID Control

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations

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