A reinforcement learning method for human-robot collaboration in assembly tasks

2022 ◽  
Vol 73 ◽  
pp. 102227
Author(s):  
Rong Zhang ◽  
Qibing Lv ◽  
Jie Li ◽  
Jinsong Bao ◽  
Tianyuan Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Method ◽  
Assembly Tasks ◽  
Human Robot Collaboration

Adaptive Reinforcement Learning and Its Application to Robot Compliance Learning

1995 ◽  
Vol 7 (3) ◽  
pp. 250-262 ◽  
Author(s):  
Boo-Ho Yang ◽  
◽  
Haruhiko Asada
Keyword(s):  
Reinforcement Learning ◽  
Force Feedback ◽  
Learning Algorithm ◽  
Robotic Assembly ◽  
Control Law ◽  
Learning Method ◽  
Compliance Control ◽  
Connectionist Networks ◽  
New Learning ◽  
Assembly Tasks

A new learning algorithm for connectionist networks that solves a class of optimal control problems is presented. The algorithm, called Adaptive Reinforcement Learning Algorithm, employs a second network to model immediate reinforcement provided from the task environment and adaptively identities it through repeated experience. Output perturbation and correlation techniques are used to translate mere critic signals into useful learning signals for the connectionist controller. Compared with the direct approaches of reinforcement learning, this algorithm shows faster and guaranteed improvement in the control performance. Robustness against inaccuracy of the model is also discussed. It is demonstrated by simulation that the adaptive reinforcement learning method is efficient and useful in learning a compliance control law in a class of robotic assembly tasks. A simple box palletizing task is used as an example, where a robot is required to move a rectangular part to the corner of a box. In the simulation, the robot is initially provided with only predetermined velocity command to follow the nominal trajectory. At each attempt, the box is randomly located and the part is randomly oriented within the grasp of the end-effector. Therefore, compliant motion control is necessary to guide the part to the corner of the box while avoiding excessive reaction forces caused by the collision with a wall. After repeating the failure in performing the task, the robot can successfully learn force feedback gains to modify its nominal motion. Our results show that the new learning method can be used to learn a compliance control law effectively.

A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

2009 ◽  
Vol 129 (7) ◽  
pp. 1253-1263
Author(s):  
Toru Eguchi ◽  
Takaaki Sekiai ◽  
Akihiro Yamada ◽  
Satoru Shimizu ◽  
Masayuki Fukai
Keyword(s):  
Reinforcement Learning ◽  
Control Technology ◽  
Learning Method ◽  
Plant Control

Control of an Inverted Pendulum by Reinforcement Learning Method in PLC Environment

2020 ◽  
Author(s):  
Gokhan Demirkiran ◽  
Ozcan Erdener ◽  
Onay Akpinar ◽  
Pelin Demirtas ◽  
M. Yagiz Arik ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Method

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

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