Full metadata
Title
Autonomous System Control of Multiple Robotic Arms Collaboration via Machine Learning
Description
Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms.
Thus, incentivizing collaboration and preventing collisions are the two principles which
are followed by the agent during the training process. Nowadays, more and more
applications, both in industry and daily lives, require at least two arms, instead of
requiring only a single arm. A dual-arm robot satisfies much more needs of different
types of tasks, such as folding clothes at home, making a hamburger in a grill or
picking and placing a product in a warehouse.
The applications done in this paper are all about object pushing. This thesis
focuses on how to train the agent to learn pushing an object away as far as possible.
Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then
utilized in this paper to train the agent to generate optimal actions. Deep Deterministic
Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL
methods used in this thesis.
Date Created
2023
Contributors
- Lin, Steve (Author)
- Ben Amor, Hani (Thesis advisor)
- Redkar, Sangram (Committee member)
- Zhang, Yu (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
48 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.189299
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Computer Engineering
System Created
- 2023-08-28 05:01:09
System Modified
- 2023-08-28 05:01:14
- 1 year 2 months ago
Additional Formats