Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks

Traub, Manuel; Legenstein, Robert; Otte, Sebastian

doi:10.1109/iros51168.2021.9636001

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…These works demonstrated the competitiveness of LSNN compared to other similar second-generation networks like LSTM (Long Short-Term Memory). In [ 26 ], the authors propose the use of this recurrent SNN to learn the kinematic model and exert control over a trunk-type robotic arm. They showcased its capability to effectively control such robots with up to 25-DoF with nearly millimeter precision.…”

Section: Snn In Robotic Controlmentioning

confidence: 99%

“…For decades, the true potential of these networks has not been realized in practice. However, recent advancements suggest a shift in this trend, with promising progress in neuromorphic hardware and methodologies [ 26 ]. New strategies have emerged to tackle the SNN training challenge, including evolutionary algorithms, Liquid State Machines (LSM), and the neuroscience-inspired STDP rule.…”

Section: Snn In Robotic Controlmentioning

confidence: 99%

“…Subsequently, using Equation (4), in the connection to the u ensemble, a linear transformation is applied to these values, multiplying them by their respective gains 𝑘 , 𝑘 , and 𝑘 . Finally, the 𝑢 ensemble performs a weighted sum of the three branches of the controller according to Equation (26) and outputs the appropriate torque signal for the robot's movement.…”

Section: Controller Designmentioning

confidence: 99%

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A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Marrero,

Kern,

Urrea

2024

Sensors

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This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

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Section: Snn In Robotic Controlmentioning

confidence: 99%

Section: Snn In Robotic Controlmentioning

confidence: 99%

Section: Controller Designmentioning

confidence: 99%

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A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Marrero,

Kern,

Urrea

2024

Sensors

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“…[25]). We denote the rotation of a vector u by the quaternion q with q u. Quaternions provide several advantages when dealing with rotations in the context of neural networks [26]. Preliminary experiments indicated that quaternions work significantly better than Euler angles, which were employed previously [17].…”

Section: A Perspective Takingmentioning

confidence: 99%

Binding Dancers Into Attractors

Kaltenberger¹,

Otte²,

Butz³

2022

Preprint

Self Cite

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To effectively perceive and process observations in our environment, feature binding and perspective taking are crucial cognitive abilities. Feature binding combines observed features into one entity, called a Gestalt. Perspective taking transfers the percept into a canonical, observer-centered frame of reference. Here we propose a recurrent neural network model that solves both challenges. We first train an LSTM to predict 3D motion dynamics from a canonical perspective. We then present similar motion dynamics with novel viewpoints and feature arrangements. Retrospective inference enables the deduction of the canonical perspective. Combined with a robust mutualexclusive softmax selection scheme, random feature arrangements are reordered and precisely bound into known Gestalt percepts. To corroborate evidence for the architecture's cognitive validity, we examine its behavior on the silhouette illusion, which elicits two competitive Gestalt interpretations of a rotating dancer. Our system flexibly binds the information of the rotating figure into the alternative attractors resolving the illusion's ambiguity and imagining the respective depth interpretation and the corresponding direction of rotation. We finally discuss the potential universality of the proposed mechanisms.

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“…This evidence led us to we investigate the role of coding with specific patterns of spikes by introducing a parameter that defines the tolerance to precise spike timing during learning. Although many studies have approached learning in feedforward [9,[18][19][20][21][22] and recurrent spiking networks [2,3,8,10,12,23,24], a very small number of them successfully faced real world problems and reinforcement learning tasks [3,25]. In this work, we apply our framework to the problem of behavioral cloning in recurrent spiking networks and show how it produces valid solutions for relevant tasks (button-and-food and the 2D Bipedal Walker).…”

Section: Introductionmentioning

confidence: 99%

Error-based or target-based? A unified framework for learning in recurrent spiking networks

2022

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The field of recurrent neural networks is over-populated by a variety of proposed learning rules and protocols. The scope of this work is to define a generalized framework, to move a step forward towards the unification of this fragmented scenario. In the field of supervised learning, two opposite approaches stand out, error-based and target-based. This duality gave rise to a scientific debate on which learning framework is the most likely to be implemented in biological networks of neurons. Moreover, the existence of spikes raises the question of whether the coding of information is rate-based or spike-based. To face these questions, we proposed a learning model with two main parameters, the rank of the feedback learning matrix R and the tolerance to spike timing τ⋆. We demonstrate that a low (high) rank R accounts for an error-based (target-based) learning rule, while high (low) tolerance to spike timing promotes rate-based (spike-based) coding. We show that in a store and recall task, high-ranks allow for lower MSE values, while low-ranks enable a faster convergence. Our framework naturally lends itself to Behavioral Cloning and allows for efficiently solving relevant closed-loop tasks, investigating what parameters ( R , τ ⋆ ) are optimal to solve a specific task. We found that a high R is essential for tasks that require retaining memory for a long time (Button and Food). On the other hand, this is not relevant for a motor task (the 2D Bipedal Walker). In this case, we find that precise spike-based coding enables optimal performances. Finally, we show that our theoretical formulation allows for defining protocols to estimate the rank of the feedback error in biological networks. We release a PyTorch implementation of our model supporting GPU parallelization.

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Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks

Cited by 8 publications

References 13 publications

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Binding Dancers Into Attractors

Error-based or target-based? A unified framework for learning in recurrent spiking networks

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