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Wei-Hsin Yen

Bio: Wei-Hsin Yen is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Robot control & Robot. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

Papers
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Journal ArticleDOI
TL;DR: The robotic experiments demonstrate that the proposed robot peer reciprocal learning system can help robots achieve difficult tasks in appropriate and cooperative ways, just as humans do.
Abstract: This paper proposes a robot peer reciprocal learning system in which robot peers can not only cooperatively accomplish a difficult task but also help each other to learn better. In this system, each robot is an independent individual and has the ability to make individual decisions. They can communicate about image information, individual decisions, and current state to formulate mutual decisions and motions. For learning a new concept, we propose a mutual learning method, which allows the robots to learn from each other by exchanging weights in their neural network concept learning system. The simulation results show that the robots can learn from each other to build general concepts from limited training, while improving both of their performances at the same time. Finally, we design two cooperative tasks, which require the robots to formulate mutual sequential motions and keep communicating to manage their motions. The robotic experiments demonstrate that the proposed robot peer reciprocal learning system can help robots achieve difficult tasks in appropriate and cooperative ways, just as humans do.

5 citations

Proceedings ArticleDOI
29 May 2015
TL;DR: The semi-automatically SLAM method is composed of SLAM, obstacle avoidance strategies and following technique, which can follow human operator walking around the surrounding, and build the environment map at the same time.
Abstract: This paper mainly discusses the design and implementation of semi-automatically simultaneous localization and mapping (SLAM). SLAM is an important technique for home service robots to move in unknown environments and built the environment map. Different from traditional SLAM method which is operated by a remote controller, the semi-automatically SLAM is operated by human-robot interaction. The semi-automatically SLAM method is composed of SLAM, obstacle avoidance strategies and following technique. With this method, the robot can follow human operator walking around the surrounding, and build the environment map at the same time. The SLAM system is built using the Iterative Closest Point (ICP) algorithm. The ICP algorithm estimates the pose of the robot and compares the prior built map with current laser information to iteratively revise the environment map. Furthermore, Q-learning is applied for obstacle avoidance during navigation. After learning, the robot can navigates smoothly and avoid obstacles automatically. The proposed methods experimented in the laboratory and in the RoboCup Japan Open 2014 competition. The validity and efficiency of the semi-automatically SLAM for the home service robot are demonstrated.

Cited by
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Journal ArticleDOI
TL;DR: The authors found that when students answer an in-class conceptual question individually using clickers, discuss it with their neighbors, and then revote on the same question, the percentage of correct answers typically increases.

233 citations

Journal ArticleDOI
TL;DR: A workflow-net based framework for agent cooperation is proposed to enable collaboration among fog computing devices and form a cooperative IoT service delivery system and results show that the cooperation process increases the number of achieved tasks and is performed in a timely manner.
Abstract: Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device’s capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we propose a workflow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of workflow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from different fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner.

57 citations

Journal ArticleDOI
TL;DR: This work proposes a teaching method based on Imitation and a learning method that incorporates Incremental Learning and Meta Learning that makes robots capable of learning and cooperating with other robots.
Abstract: At present, cloud robots tend to be intelligent and cooperative. Based on this, we proposed a teaching method based on Imitation and a learning method that incorporates Incremental Learning and Meta Learning. We use Imitation Learning to teach robots, and more concretely, we propose a natural teaching method based on visual sense by using a depth camera, the robot can learn from the trajectory caught by the camera. Meta Learning helps robots understand the task and split it into some subtasks which enhances the level of generalization. Besides, once the circumstances change the robot can update the cloud database using Incremental Learning. Using proposed method, we make robots capable of learning and cooperating with other robots. It is no longer necessary for robots to learn based on a great number of data which is a shortcoming of traditional robots. The greatest advantage of this method is that we improve the learning efficiency of robots and enhance the level of generalization of the model. Our method was experimentally verified in a laboratory and the results indicated that the method improved the learning efficiency of robots.

2 citations