Date of Graduation
12-2020
Document Type
Thesis
Degree Name
Bachelor of Science in Mechanical Engineering
Degree Level
Undergraduate
Department
Mechanical Engineering
Advisor/Mentor
Zhou, Wenchao
Committee Member/Reader
Le, Ngan
Committee Member/Second Reader
Roe, Larry A.
Abstract
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the great achievements of deep reinforcement learning in various domains including finance,medicine, healthcare, video games, robotics and computer vision.Deep neural network was started with multi-layer perceptron (1stgeneration) and developed to deep neural networks (2ndgeneration)and it is moving forward to spiking neural networks which are knownas3rdgeneration of neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically-realistic models of neurons to carry out computation. In this thesis, we first provide a comprehensive review on both spiking neural networks and deep reinforcement learning with emphasis on robotic applications. Then we will demonstrate how to develop a robotics application for context-aware scene understanding to perform sensorimotor coupling. Our system contains two modules corresponding to scene understanding and robotic navigation. The first module is implemented as a spiking neural network to carry out semantic segmentation to understand the scene in front of the robot. The second module provides a high-level navigation command to robot, which is considered as an agent and implemented by online reinforcement learning. The module was implemented with biologically plausible local learning rule that allows the agent to adopt quickly to the environment. To benchmark our system, we have tested the first module on Oxford-IIIT Pet dataset and the second module on the custom-made Gym environment. Our experimental results have proven that our system is able present the competitive results with deep neural network in segmentation task and adopts quickly to the environment.
Keywords
Spiking Neural Networks; Online Learning; Robotics
Citation
Yamazaki, K. (2020). Towards Sensorimotor Coupling of a Spiking Neural Network and Deep Reinforcement Learning for Robotics Application. Mechanical Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/meeguht/98
