Manipulation task planning for heterogeneous object stacking based on reinforcement learning

The paper propose a new way of solving the Pallet Packing Problem by modelling it as a Markov decision process. This allows the program to make decisions step-by-step based only on the current state and adapt to any error in execution. By applying reinforcement learning techniques, an agent can be t...

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Bibliographic Details
Main Author:	Pham, Minh Khang
Other Authors:	Domenico Campolo
Format:	Final Year Project
Language:	English
Published:	2019
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics DRNTU::Engineering::Mechanical engineering::Robots
Online Access:	http://hdl.handle.net/10356/76393
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-76393
record_format	dspace
spelling	sg-ntu-dr.10356-763932023-03-04T18:59:38Z Manipulation task planning for heterogeneous object stacking based on reinforcement learning Pham, Minh Khang Domenico Campolo School of Mechanical and Aerospace Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics DRNTU::Engineering::Mechanical engineering::Robots The paper propose a new way of solving the Pallet Packing Problem by modelling it as a Markov decision process. This allows the program to make decisions step-by-step based only on the current state and adapt to any error in execution. By applying reinforcement learning techniques, an agent can be trained from simulation to learn a model-free near-optimal policy that maximize the discounted cumulative rewards, which is proportional to the original objective function. Experiments show positive results on simulations involving packing up to 12 boxes into a grid-based pallet of size 886. Bachelor of Engineering (Mechanical Engineering) 2019-01-07T00:32:41Z 2019-01-07T00:32:41Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/76393 en Nanyang Technological University 38 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics DRNTU::Engineering::Mechanical engineering::Robots
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics DRNTU::Engineering::Mechanical engineering::Robots Pham, Minh Khang Manipulation task planning for heterogeneous object stacking based on reinforcement learning
description	The paper propose a new way of solving the Pallet Packing Problem by modelling it as a Markov decision process. This allows the program to make decisions step-by-step based only on the current state and adapt to any error in execution. By applying reinforcement learning techniques, an agent can be trained from simulation to learn a model-free near-optimal policy that maximize the discounted cumulative rewards, which is proportional to the original objective function. Experiments show positive results on simulations involving packing up to 12 boxes into a grid-based pallet of size 886.
author2	Domenico Campolo
author_facet	Domenico Campolo Pham, Minh Khang
format	Final Year Project
author	Pham, Minh Khang
author_sort	Pham, Minh Khang
title	Manipulation task planning for heterogeneous object stacking based on reinforcement learning
title_short	Manipulation task planning for heterogeneous object stacking based on reinforcement learning
title_full	Manipulation task planning for heterogeneous object stacking based on reinforcement learning
title_fullStr	Manipulation task planning for heterogeneous object stacking based on reinforcement learning
title_full_unstemmed	Manipulation task planning for heterogeneous object stacking based on reinforcement learning
title_sort	manipulation task planning for heterogeneous object stacking based on reinforcement learning
publishDate	2019
url	http://hdl.handle.net/10356/76393
_version_	1759855441959976960

Manipulation task planning for heterogeneous object stacking based on reinforcement learning

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