2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)最新文献

Pseudo- Randomization in Automating Robot Behaviour during Human-Robot Interaction 人机交互过程中自动化机器人行为的伪随机化

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278115

S. Paplu, Chinmaya Mishra, K. Berns

引用次数: 5

Tactile-based curiosity maximizes tactile-rich object-oriented actions even without any extrinsic rewards 基于触觉的好奇心最大化了触觉丰富的面向对象行为，即使没有任何外部奖励

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278082

Hiroki Mori, Masayuki Masuda, T. Ogata

引用次数: 1

Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders 基于深度自编码器的主动高效编码自校准主动双目视觉

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278037

Charles Wilmot, Bertram E. Shi, J. Triesch

引用次数: 2

Modeling robot co-representation: state-of-the-art, open issues, and predictive learning as a possible framework 机器人共同表示建模:最先进的、开放的问题，以及作为可能框架的预测学习

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278031

M. Kirtay, Olga A. Wudarczyk, D. Pischedda, A. Kuhlen, R. A. Rahman, J. Haynes, V. Hafner

{"title":"Modeling robot co-representation: state-of-the-art, open issues, and predictive learning as a possible framework","authors":"M. Kirtay, Olga A. Wudarczyk, D. Pischedda, A. Kuhlen, R. A. Rahman, J. Haynes, V. Hafner","doi":"10.1109/ICDL-EpiRob48136.2020.9278031","DOIUrl":"https://doi.org/10.1109/ICDL-EpiRob48136.2020.9278031","url":null,"abstract":"Robots are getting increasingly more present in many spheres of human life, making the need for robots that can successfully engage in natural social interactions with humans paramount. Successful human-robot interaction could be achieved more effectively if robots could act predictably and could predict the humans' actions. If robots could represent human partners and generate behaviors that are in line with the partners' expectations based on human's mental models of interdependent action, human agents would be able to apply predictive and adaptive mechanisms acquired in human interactions to interact with robots effectively. How could robots be predictable and be capable of predicting human behavior? We propose that this could be achieved by having an internal representation of both oneself and the other agent, that is by equipping the robot with the ability to co-represent. Here, co-representation refers to the representation of the partner's actions alongside one's own actions. Although co-representation constitutes an essential process for successful human social interaction, as it supports understanding of others' actions, to date co-representation processes have only scarcely been integrated into robotic platforms. We highlight the state-of-the-art findings on co-representation in social robotics, discuss current research limitations and open issues for creating computational models of co-representation in robots, and put forward the idea that predictive learning might constitute a particularly promising framework to build models of co-representing robots. Overall, in this article, we offer an integrated view of the state-of-the-art findings in robotics literature on co-representation and outline directions for future research, with the aim to boost success in building robots equipped with co-representation models fit for smooth social interactions.","PeriodicalId":114948,"journal":{"name":"2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117056967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Involuntary movement suppression filter for electric wheelchair with athetosis-type cerebral palsy 电动轮椅伴手足动型脑瘫的不自主运动抑制过滤器

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278065

Motoyu Katsumura, K. Yano, T. Nakao, Atsushi Hamada, Katsuhiko Torii

引用次数: 1

The Need for MORE: Need Systems as Non-Linear Multi-Objective Reinforcement Learning 需要更多:需要系统作为非线性多目标强化学习

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278062

Matthias Rolf

{"title":"The Need for MORE: Need Systems as Non-Linear Multi-Objective Reinforcement Learning","authors":"Matthias Rolf","doi":"10.1109/ICDL-EpiRob48136.2020.9278062","DOIUrl":"https://doi.org/10.1109/ICDL-EpiRob48136.2020.9278062","url":null,"abstract":"Both biological and artificial agents need to coordinate their behavior to suit various needs at the same time. Reconciling conflicts of different needs and contradictory interests such as self-preservation and curiosity is the central difficulty arising in the design and modelling of need and value systems. Current models of multi-objective reinforcement learning do either not provide satisfactory power to describe such conflicts, or lack the power to actually resolve them. This paper aims to promote a clear understanding of these limitations, and to overcome them with a theory-driven approach rather than ad hoc solutions. The first contribution of this paper is the development of an example that demonstrates previous approaches' limitations concisely. The second contribution is a new, non-linear objective function design, MORE, that addresses these and leads to a practical algorithm. Experiments show that standard RL methods fail to grasp the nature of the problem and ad-hoc solutions struggle to describe consistent preferences. MORE consistently learns a highly satisfactory solution that balances contradictory needs based on a consistent notion of optimality.","PeriodicalId":114948,"journal":{"name":"2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128188179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

From Reward to Histone: Combining Temporal-Difference Learning and Epigenetic Inheritance for Swarm's Coevolving Decision Making 从奖励到组蛋白:结合时间差异学习和表观遗传的群体协同进化决策

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278049

F. Mukhlish, J. Page, Michael Bain

{"title":"From Reward to Histone: Combining Temporal-Difference Learning and Epigenetic Inheritance for Swarm's Coevolving Decision Making","authors":"F. Mukhlish, J. Page, Michael Bain","doi":"10.1109/ICDL-EpiRob48136.2020.9278049","DOIUrl":"https://doi.org/10.1109/ICDL-EpiRob48136.2020.9278049","url":null,"abstract":"Applying intelligence to a group of simple robots known as swarm robots has become an exciting technology in assisting or replacing humans to fulfil complex, dangerous and harsh missions. However, building a strategy for a swarm to thrive in a dynamic environment is challenging because of control decentralisation and interactions between agents. The decision-making process in a robotic task commonly takes place in sequential stages. By understanding the subsequent action-reaction process, a strategy to make optimal decisions in a respective environment can be learnt. Hence, using the concept of epigenetic inheritance, novel evolutionary-learning mechanisms for a swarm will be discussed in this paper. Reinforcement evolutionary learning using epigenetic inheritance (RELEpi) is proposed in this article. This method utilizes reward, temporal difference and epigenetic inheritance to approximate optimal action and behaviour policies. The proposed method opens possibilities to combine reward-based learning and evolutionary methods as a stacked process where histone value is used rather than fitness function. The formulation consists of methylation and epigenetic mechanisms, inspired by the epigenome studies. The methylation process helps the accumulation of the reward to histone value of the gene. Epigenetic mechanisms give the ability to mate genetic information along with their histone value.","PeriodicalId":114948,"journal":{"name":"2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130156473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-level representations through unconstrained sensorimotor learning 无约束感觉运动学习的高级表征

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278100

Ozgur Baran Ozturkcu, Emre Ugur, Erhan Öztop

引用次数: 3

Integration of Vergence, Cyclovergence, and Saccades through Active Efficient Coding 通过主动高效编码集成收敛、循环收敛和扫视

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278126

Qingpeng Zhu, J. Triesch, Bertram E. Shi

引用次数: 3

Fast Developmental Stereo-Disparity Detectors 快速发展立体视差探测器

2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) Pub Date : 2020-10-26 DOI: 10.1109/ICDL-EpiRob48136.2020.9278056

J. A. Knoll, Van-Nam Hoang, Jacob Honer, Samuel Church, Thanh-Hai Tran, J. Weng

{"title":"Fast Developmental Stereo-Disparity Detectors","authors":"J. A. Knoll, Van-Nam Hoang, Jacob Honer, Samuel Church, Thanh-Hai Tran, J. Weng","doi":"10.1109/ICDL-EpiRob48136.2020.9278056","DOIUrl":"https://doi.org/10.1109/ICDL-EpiRob48136.2020.9278056","url":null,"abstract":"Traditional methods for stereo-disparity detection use explicit search between the left and right images. Although such methods are simple and intuitive for understanding, they suffer from degeneracies when the search window contains weak texture. Developmental Networks (DNs) are task-nonspecific and modality-nonspecific learning engines. Because they are general-purpose learners, they have a potential to deal with many types of degeneracies in intelligent systems. This work presents two novel mechanisms to deal with degeneracies: volume dimension and subwindow voting. While developmental stereo-disparity detection has been tested on simulated stereo images in our prior publications, it has never been tested on the real world. This paper reports our system, $3mathrm{DEye}$, which is the first to have filled this void. The algorithm, software, graphical user interface, training, performance, and update rates on CPU and GPU, respectively, on a Sony G8142 mobile phone are reported. Many deep learning methods that use error back-propagation suffer from the controversy of “post-selection” using the test set [1], to select one from many networks to report. In contrast, all randomly initialized DNs are performance-equivalent, no “post-selection” using test set. Possible future improvements for practical real-world and real-time applications are discussed.","PeriodicalId":114948,"journal":{"name":"2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116871399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1