Please use this identifier to cite or link to this item:
Title: Machine learning for human robot collaboration
Authors: Pranay, Mathur
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Pranay, M. (2022). Machine learning for human robot collaboration. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: A3246-211
Abstract: With the advent of technology, autonomous robotic agents have attained an increasingly important role in various industries such as manufacturing, transportation or even agriculture. Despite the numerous benefits they offer, these agents still present the challenge of adapting to small changes in their tasks. An additional challenge is encountered in the effort and expertise required to teach an agent a new skill. This process can be simplified through Imitation Learning, which aims to teach an agent new skills by exposing it to expert demonstrations. This project explores the use of Generative Adversarial Imitation Learning to train a robotic simulation on the OpenAI Gym framework. Further, this project examines the effectiveness of pre-training and Spectral Normalization on the performance of the trained agent and the speed and stability of the training process. Through extensive experimentation, this project determines that using Proximal Policy Optimization instead of Trust-Region Policy Optimization can enhance agent performance. Additionally, this project shows that pre-training can accelerate learning speed, and spectral normalization can improve training stability.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
Final FYP Report2.39 MBAdobe PDFView/Open

Page view(s)

Updated on Mar 3, 2024


Updated on Mar 3, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.