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2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)最新文献

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Evolutionary conditions for the emergence of robotic theory of mind with multiple goals 具有多重目标的机器人心智理论出现的进化条件
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607928
Kyung-Joong Kim, K. Eo, Ye Ran Jung, Si On Kim, Sung-Bae Cho
{"title":"Evolutionary conditions for the emergence of robotic theory of mind with multiple goals","authors":"Kyung-Joong Kim, K. Eo, Ye Ran Jung, Si On Kim, Sung-Bae Cho","doi":"10.1109/RiiSS.2013.6607928","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607928","url":null,"abstract":"Theory of mind (ToM) has been recognized as one of important cognitive functions for human beings and allows us to understand/model other's mind from their facial expression, verbal conversations, and behaviors. It has been known that the functionality has been developed in the early stage of our life. Recently, there have been some works on developing automated algorithms for robotic theory of mind. Similar to human's ToM, the process collects information on other robots and infers the internal states of them using estimation-exploration algorithms (EEA). Although they're successful, they assume that the playground has only one simple target to pursue. In real-world settings, the environment has several targets giving confusion to observers. In this study, we attempt to test the robotic theory of mind in the existence of multiple goals. Experimental results show that there are some successful conditions to get the best theory of mind capability for robots with a noisy target. This study gives insight on the human's theory of mind robust to the existence of multiple goals.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122075076","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: 4
Cyclic motion generation for intelligent robot by evolutionary computation 基于进化计算的智能机器人循环运动生成
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607923
János Botzheim, Noriko Takase, N. Kubota
{"title":"Cyclic motion generation for intelligent robot by evolutionary computation","authors":"János Botzheim, Noriko Takase, N. Kubota","doi":"10.1109/RiiSS.2013.6607923","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607923","url":null,"abstract":"In this paper we propose a method for motion generation of intelligent multi-legged robot using evolutionary computation. Legged robot can walk in various complex terrains such as stairs as well as in flat environment. However, setting the robot's behavior to adapt to various environments in advance is very difficult. The robot can mimic the movement of organisms based on computational intelligence. In this study we apply steady state genetic algorithm for generating the motion sequence of a six-legged robot modeled by forward kinematics. The number of intermediate positions of the motion sequence is adapted to the environment and optimized as well. We use a computer simulation environment before we apply our method in real robot. This can reduce the time spent on finding the optimal parameter settings and the solution itself for the optimization problem. The solution is evaluated mainly on the moving distance of the robot. Experiments were conducted to confirm the proposed technique.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123995253","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: 3
A concurrent approach to robot team learning 机器人团队学习的并行方法
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607929
Larry Ng, M. Emami
{"title":"A concurrent approach to robot team learning","authors":"Larry Ng, M. Emami","doi":"10.1109/RiiSS.2013.6607929","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607929","url":null,"abstract":"Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt, and enhance team behaviours while improving their individual performance simultaneously. To address this issue, a cooperative learning algorithm is modified in this paper to accommodate for the individualistic Q-Learning as well as the collaborative advice sharing. The developed methods are examined in relation to the performance characteristics of single-robot learning to ascertain if they retain viable learning characteristics despite the integration of individual learning into team behaviour. Further, a modification to the individual learning method was implemented into the proposed multi-robot learning approach to examine the performance improvements gained by the multi-robot learning algorithms.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133947284","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: 4
Proposal of learning method which selects objectives based on the state 提出一种基于状态选择目标的学习方法
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607938
Hironori Miura, K. Kurashige
{"title":"Proposal of learning method which selects objectives based on the state","authors":"Hironori Miura, K. Kurashige","doi":"10.1109/RiiSS.2013.6607938","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607938","url":null,"abstract":"Reinforcement learning (RL) is one of the methods for robot action learning. RL is formulated as the maximization of a single reward; however, in most practical problems, multiple objectives need to be considered. Therefore, it is necessary to perform multi-objective optimization. We focus on the required objectives that depended on the state of the robot and propose a multi-objective optimization for the required objectives. If there is more than one required objective, multi-objective optimization is performed based on the priority of each objective. In this paper, we give two objectives to a robot and perform simulation experiments. We will demonstrate the validity of the proposed system using the simulation results.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"434 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122733775","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
Approaches to dynamic team sizes 动态团队规模的方法
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607931
G. Nitschke, S. M. Tolkamp
{"title":"Approaches to dynamic team sizes","authors":"G. Nitschke, S. M. Tolkamp","doi":"10.1109/RiiSS.2013.6607931","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607931","url":null,"abstract":"This paper elucidates the impact of comparative mechanisms for dynamically adapting team sizes in simulated robot teams that must accomplish a collective construction task. The first approach adapts team size using genotypic diversity of team behaviors in a population of behaviors. The second approach adapts team size as a function of behavioral success (degree of task accomplishment). Task complexity in the collective construction task is equated with the degree of cooperation required between robots for task accomplishment. Task complexity, and hence the most suitable team size, is not known a priori. Results indicate that adapting team size based on genotype diversity is beneficial for collective construction when the task requires a low degree of cooperation. However, when a relatively high degree of cooperation is required for task accomplishment, then dynamically adapting team size according to genotype diversity or behavioral efficacy yields comparable results.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130850772","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: 3
Application of magnetic type tactile sensor to gripper 磁性触觉传感器在夹持器上的应用
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607922
Hiroyuki Nakamoto, S. Takenawa
{"title":"Application of magnetic type tactile sensor to gripper","authors":"Hiroyuki Nakamoto, S. Takenawa","doi":"10.1109/RiiSS.2013.6607922","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607922","url":null,"abstract":"We have proposed a magnetic type tactile sensor which can measure a three-axis displacement and a three-axis force. The sensor has a simple structure which is composed of two layers, an elastic layer and a substrate layer. The elastic layer is made of an elastic material and houses cylindrical permanent magnets inside. The substrate layer is a glass epoxy board and has giant magneto resistance (GMR) elements. Feature of this sensor is a wiring reduction between two layers. It is easy to replace the elastic layer after a long usage. The components of the sensor are not expensive, and the cost of the commercial manufacturing can be lowered. In this paper, we describe a structure of the sensor. We design a sensor which has two magnets inside, and confirm the performances of a prototype through the laboratory experiments. Additionally, we apply the sensor to a robotic gripper. We verify that the gripper is able to pinch some soft or hard objects with a simple threshold condition.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129499781","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: 4
Integration of behaviors and languages with a hierarchal structure self-organized in a neuro-dynamical model 行为和语言的整合，在神经动力学模型中自组织的层次结构
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607934
T. Ogata, HIroshi G. Okuno
{"title":"Integration of behaviors and languages with a hierarchal structure self-organized in a neuro-dynamical model","authors":"T. Ogata, HIroshi G. Okuno","doi":"10.1109/RiiSS.2013.6607934","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607934","url":null,"abstract":"This paper proposes an approach for robots to ac-quire language grounding in their robot's sensory-motor flow using neuro-dynamical models. We trained our neuro-dynamical model over a set of sentences represented as sequences of characters. For the integrated recognition, we introduced a cognitive hypothesis for integrated recognition where a human's brain separately processed the “structure” and “contents” of a sentence. Our model was trained with the spelling of words and their semantic role emerged in the first model. As a result of binding the model with sensory-motion patterns, we confirmed that it could associate proper word spellings with a sensory-motor flows and a semantic roles, even if an observed flow had not been learned.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128613902","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: 13
A distributed multi robot SLAM system for environment learning 面向环境学习的分布式多机器人SLAM系统
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607933
S. R. U. N. Jafri, R. Chellali
{"title":"A distributed multi robot SLAM system for environment learning","authors":"S. R. U. N. Jafri, R. Chellali","doi":"10.1109/RiiSS.2013.6607933","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607933","url":null,"abstract":"This paper presents a multi mobile robot simultaneous localization and mapping (SLAM) system for feature based environment learning by using team of exploring robots. The environmental information is measured through the dynamic sensor network in the shape of moving robots with unknown initial poses. Each robot is equipped with 2D laser scanner and a webcam and it serves as a moving sensor node to perceive horizontal and vertical line features respectively. All the moving nodes are responsible to build the informational structured space. The proposed system is using a unified Extended Kalman Filter (EKF) based SLAM framework for each robot which eventually builds a line feature based partial 3D model of the environment. Each moving robotic sensor node then shares its feature based map model to other moving nodes which are in communication range. A global map model is then transformed after getting mutual pose estimation of the robots by matching mutual common map features in addition by taking visual confirmation of other robot. The proposed system has been tested in an indoor environment and results are shown in the paper.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133854781","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: 16
A path planning behavior learning of six legged robot based on human teaching model 基于人类教学模型的六足机器人路径规划行为学习
2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) Pub Date : 2013-04-16 DOI: 10.1109/RiiSS.2013.6607937
I. A. Sulistijono, Marianus Bayu Aji Kurniawan, Setiawardhana, Eka Prasetiono
{"title":"A path planning behavior learning of six legged robot based on human teaching model","authors":"I. A. Sulistijono, Marianus Bayu Aji Kurniawan, Setiawardhana, Eka Prasetiono","doi":"10.1109/RiiSS.2013.6607937","DOIUrl":"https://doi.org/10.1109/RiiSS.2013.6607937","url":null,"abstract":"This paper deals with a six legged robot path planning behavior learning based on human teaching. The six legged robot requires several capabilities such as perceiving, acting, communicating, and surviving. Furthermore, the six legged robot should learn various behaviors through the interaction with owner (human). This paper focuses on teaching path planning to the robot. Basically, the owner can teach the path planning by simple communication based on trial and error. Human teaches the path planning by using his hand. It will give an input of fuzzy controller used for the input and output movement of legged robot. Generally, legged robot will learn some pattern by a number of learning iteration. In every iterations the robot will have some punishment and reward coefficient of reinforcement learning used to generate better population of Genetic Algorithm until it can reach its minimum level of punishment. Experimental result shows that the legged robot can learn the path planning using the proposed method through the interaction with owner.","PeriodicalId":337659,"journal":{"name":"2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115796862","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
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