Date of Award
2024
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Electrical and Computer Engineering (MS)
Administrative Home Department
Department of Electrical and Computer Engineering
Advisor 1
Hongyu An
Committee Member 1
Tan Chen
Committee Member 2
Yan Zhang
Abstract
Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm is crucial to overcoming the challenges of deep learning in environments where data and energy are limited. Taking inspiration from this natural learning process, recent advancements [6, 7] have been made in implementing associative learning in artificial systems. This work introduces a pioneering approach by integrating associative learning utilizing an Unmanned Ground Vehicle (UGV) in conjunction with neuromorphic hardware, specifically the XyloA2TestBoard from SynSense, to facilitate online learning scenarios. The system simulates standard associative learning, like the spatial and memory learning observed in rats in a T-maze environment, without any pretraining or labeled datasets. The UGV, akin to the rats in a T-maze, autonomously learns the cause-and-effect relationships between different stimuli, such as visual cues and vibration or audio and visual cues, and demonstrates learned responses through movement. The neuromorphic robot in this system, equipped with SynSense’s neuromorphic chip, processes audio signals with a specialized Spiking Neural Network (SNN) and neural assembly, employing the Hebbian learning rule to adjust synaptic weights throughout the learning period. The XyloA2TestBoard uses little power (17.96 µW on average for logic Analog Front End (AFE) and 213.94 µW for IO circuitry), which shows that neuromorphic chips could work well in places with limited energy, offering a promising direction for advancing associative learning in artificial systems.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Siddique, Md Abu Bakr, "The Integration of Neuromorphic Computing in Autonomous Robotic Systems", Open Access Master's Thesis, Michigan Technological University, 2024.
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Artificial Intelligence and Robotics Commons, Computational Engineering Commons, Electrical and Computer Engineering Commons, Hardware Systems Commons, Robotics Commons