Intelligent robot manipulation with deep learning
The application of reinforcement learning (RL) in robotics has seen significant advancements across various sectors, yet a critical challenge persists: the simulation-to-reality (sim-to-real) gap. In real-world scenarios, robots frequently underperform due to their inability to access or accurate...
Saved in:
Main Author: | Tan, Jun Aun |
---|---|
Other Authors: | Lin Zhiping |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/176388 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges
by: Bhagat, S., et al.
Published: (2021) -
Imitate the good and avoid the bad: An incremental approach to safe reinforcement learning
by: HOANG, Minh Huy, et al.
Published: (2024) -
Stealing deep reinforcement learning models for fun and profit
by: CHEN, Kangjie, et al.
Published: (2021) -
Behavior imitation for manipulator control and grasping with deep reinforcement learning
by: Liu, Qiyuan
Published: (2024) -
HIERARCHICAL REINFORCEMENT LEARNING WITH PARAMETERIZED OPTIONS FOR LONG-HORIZON ROBOTIC MANIPULATION
by: GUO CHAOQUN
Published: (2023)