Reciprocal Learning for Robot Peers
01 Jan 2017-IEEE Access (Institute of Electrical and Electronics Engineers Inc.)-Vol. 5, pp 6198-6211
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.
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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.
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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
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.
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Cites background from "Reciprocal Learning for Robot Peers..."
...A robot mutual learning system, each robot in the system is individual but they can also exchange information help each other learn [6]....
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References
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TL;DR: This paper analyzes the literature from the point of view of swarm engineering and proposes two taxonomies: in the first taxonomy, works that deal with design and analysis methods are classified; in the second, works according to the collective behavior studied are classified.
Abstract: Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.
1,405 citations
"Reciprocal Learning for Robot Peers..." refers background in this paper
...The robots can organize themselves and design robust, scalable, and flexible collective behaviors through simple rules and local interaction [33]....
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TL;DR: A review of peer learning can be found in this article, focusing mainly on peer tutoring, cooperative learning, and peer assessment, together with questions of implementation integrity and consequent effectiveness and cost-effectiveness.
Abstract: Developments in forms of peer learning 1981–2006 are reviewed, focusing mainly on peer tutoring, cooperative learning, and peer assessment. Types and definitions of peer learning are explored, together with questions of implementation integrity and consequent effectiveness and cost‐effectiveness. Benefits to helpers are now emphasised at least as much as benefits to those helped. In this previously under‐theorised area, an integrated theoretical model of peer learning is now available. Peer learning has been extended in types and forms, in curriculum areas and in contexts of application beyond school. Engagement in helping now often encompasses all community members, including those with special needs. Social and emotional gains now attract as much interest as cognitive gains. Information technology is now often a major component in peer learning, operating in a variety of ways. Embedding and sustainability has improved, but further improvement is needed.
1,273 citations
"Reciprocal Learning for Robot Peers..." refers background in this paper
...Peer-assisted learning theory provides a single theoretical model by synthesizing existing research [16] and describing how peer-assisted learning works....
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TL;DR: In this article, a review of peer tutoring in colleges and universities is presented, focusing on the effectiveness of different types and formats of PAs in terms of quality, outcomes and cost-effectiveness.
Abstract: Quality, outcomes and cost-effectiveness of methods of teaching and learning in colleges and universities are being scrutinised more closely. The increasing use of peer tutoring in this context necessitates a clear definition and typology, which are outlined. The theoretical advantages of peer tutoring are discussed and the research on peer tutoring in schools briefly considered. The substantial existing research on the effectiveness of the many different types and formats of peer tutoring within colleges and universities is then reviewed. Much is already known about the effectiveness of some types of peer tutoring and this merits wider dissemination to practitioners. Directions for future research are indicated.
1,073 citations
"Reciprocal Learning for Robot Peers..." refers background in this paper
...In this context, the student who has the best performance will be treated as a surrogate teacher and can assist another student, which is called the peer tutoring method [11]....
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...Two or more students are involved in the tutoring, with one student performing the role of ‘‘tutor’’ and the other students performing the role of ‘‘tutee’’ [11]....
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Book•
01 Jan 2002
TL;DR: What I wanted was a book like the other book and more, and I think Gibbons has managed to give us both of thes e.
Abstract: What I wanted was a book like the other book and ye t more. I wanted teachers, both mainstream and EAL specialists to be able to pick it up and experience the clear common sense of Gibbon’s writing. I wanted to extend my thinking, to be challenged by the new wor k. I think Gibbons has managed to give us both of thes e. I also wanted the new book to be more easily availabl e in this country. We have yet to see whether this wi ll be the case.
845 citations
"Reciprocal Learning for Robot Peers..." refers background in this paper
...The Scaffolding theory [1], which was inspired by the ideas of psychologist Vygotsky, posits that learning should be treated as a social process and that new cognitive skills develop as the learner receives appropriate support from an adult or a peer of high capabilities [2], [3]....
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