Pub Date : 1900-01-01DOI: 10.1109/ICARSC.2017.7964038
Lino Marques, A. Bernardino
{"title":"Welcome message","authors":"Lino Marques, A. Bernardino","doi":"10.1109/ICARSC.2017.7964038","DOIUrl":"https://doi.org/10.1109/ICARSC.2017.7964038","url":null,"abstract":"","PeriodicalId":293686,"journal":{"name":"International Conference on Autonomous Robot Systems and Competitions","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485558","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}
Pub Date : 1900-01-01DOI: 10.1109/ICARSC52212.2021.9429812
P. Stone
For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot. It then introduces two new algorithms for imitation learning from observation that enable a robot to mimic demonstrated skills from state-only trajectories, without any knowledge of the actions selected by the demonstrator. Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot, and imitation learning from observation opens the possibility of robots learning from the vast trove of videos available online. Bio: Dr. Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Chair of the Robotics Portfolio Program, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents’ Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone’s research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, robotics, and e-commerce. Professor Stone received his Ph.D. in computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff member in the Artificial intelligence Principles Research Department at AT&T Labs – Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2003, he won an NSF CAREER award for his proposed long term research on learning agents in dynamic, collaborative, and adversarial multiagent environments, in 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone cofounded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as President and COO.
为了让自主机器人在开放的、动态变化的世界中运行，它们需要能够从相对较少的经验中学习一套强大的技能。本讲座首先介绍接地模拟学习，作为一种弥合模拟器与现实世界之间所谓现实差距的方法，以实现从模拟到真实机器人的迁移学习。然后，它引入了两种新的算法，用于从观察中模仿学习，使机器人能够从状态轨迹模仿演示技能，而无需了解演示者选择的动作。基于模拟学习已经让一种广泛使用的类人机器人实现了已知最快的稳定行走，而通过观察进行模仿学习为机器人从大量在线视频中学习提供了可能性。Peter Stone博士是德克萨斯大学奥斯汀分校的David Bruton, Jr.百年纪念教授和计算机科学副主席，以及机器人投资组合计划主席。2013年，他被授予德克萨斯大学系统评委会杰出教学奖，2014年，他被选入德克萨斯大学奥斯汀杰出教师学院，并获得大学杰出教学教授的称号。Stone教授在人工智能领域的研究兴趣包括机器学习(尤其是强化学习)、多智能体系统、机器人和电子商务。Stone教授于1998年获得卡内基梅隆大学计算机科学博士学位。从1999年到2002年，他是AT&T实验室人工智能原理研究部门的高级技术人员。他是Alfred P. Sloan研究员、Guggenheim研究员、AAAI研究员、IEEE研究员、AAAS研究员、富布赖特学者和2004年ONR青年研究员。2003年，他因在动态、协作和对抗多智能体环境中对学习智能体的长期研究而获得了NSF CAREER奖，2007年，他获得了著名的IJCAI计算机和思想奖，该奖项每两年颁发给35岁以下的顶级人工智能研究人员，2016年，他获得了ACM/SIGAI自主智能体研究奖。Stone教授于2015年共同创立了Cogitai, Inc.，这是一家专注于持续学习的创业公司，目前担任总裁兼首席运营官。
{"title":"Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation","authors":"P. Stone","doi":"10.1109/ICARSC52212.2021.9429812","DOIUrl":"https://doi.org/10.1109/ICARSC52212.2021.9429812","url":null,"abstract":"For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot. It then introduces two new algorithms for imitation learning from observation that enable a robot to mimic demonstrated skills from state-only trajectories, without any knowledge of the actions selected by the demonstrator. Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot, and imitation learning from observation opens the possibility of robots learning from the vast trove of videos available online. Bio: Dr. Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Chair of the Robotics Portfolio Program, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents’ Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone’s research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, robotics, and e-commerce. Professor Stone received his Ph.D. in computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff member in the Artificial intelligence Principles Research Department at AT&T Labs – Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2003, he won an NSF CAREER award for his proposed long term research on learning agents in dynamic, collaborative, and adversarial multiagent environments, in 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone cofounded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as President and COO.","PeriodicalId":293686,"journal":{"name":"International Conference on Autonomous Robot Systems and Competitions","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121666087","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}
Pub Date : 1900-01-01DOI: 10.1109/ICARSC52212.2021.9429787
M. Sotelo
{"title":"Advanced Motion Prediction for Self-Driving Cars","authors":"M. Sotelo","doi":"10.1109/ICARSC52212.2021.9429787","DOIUrl":"https://doi.org/10.1109/ICARSC52212.2021.9429787","url":null,"abstract":"","PeriodicalId":293686,"journal":{"name":"International Conference on Autonomous Robot Systems and Competitions","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114733589","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}
Pub Date : 1900-01-01DOI: 10.1109/ICARSC55462.2022.9784803
Philipp Koch, Marco Steinbrink, S. May, A. Nüchter
This publication describes a novel approach to generic robot navigation using elevation maps based on point-region-Quadtrees. The described approach plans optimized trajectories in dependency of the robot’s morphology by detecting and rating obstacles. This is achieved by tailoring the tree based elevation map to the robot’s design. The approach and the related work, it is based on, is described in detail, experiments are provided, which verify the results.
{"title":"Traversability Analysis for Wheeled Robots using Point-Region-Quad-Tree based Elevation Maps","authors":"Philipp Koch, Marco Steinbrink, S. May, A. Nüchter","doi":"10.1109/ICARSC55462.2022.9784803","DOIUrl":"https://doi.org/10.1109/ICARSC55462.2022.9784803","url":null,"abstract":"This publication describes a novel approach to generic robot navigation using elevation maps based on point-region-Quadtrees. The described approach plans optimized trajectories in dependency of the robot’s morphology by detecting and rating obstacles. This is achieved by tailoring the tree based elevation map to the robot’s design. The approach and the related work, it is based on, is described in detail, experiments are provided, which verify the results.","PeriodicalId":293686,"journal":{"name":"International Conference on Autonomous Robot Systems and Competitions","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115638482","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}
F. J. Lera, Vicente Matellán Olivera, Miguel Ángel Conde González, F. Martín
Non-research spectators of robotic competitions perceive robot behaviors as deterministic. This perception plays a significant role in the acceptation of robots as social entities. This paper presents a motivational based architecture to generate natural autonomous robot behaviors that will help to improve the users' perception of robot's abilities. This proposal is based on Alderfer's simplification of Maslow's hierarchy of needs. A customized version of the ability test Speech Recognition & Audio Detection Test from the RoboCup competition has been implemented to illustrate how this architecture works. We discuss and analyze how the motivational variables affect the robot behaviors generated during the human-robot interaction. The preliminary tests show less deterministic robot behaviors than traditional approaches for robotic competitions.
{"title":"A Motivational Architecture to Create more Human-Acceptable Assistive Robots for Robotics Competitions","authors":"F. J. Lera, Vicente Matellán Olivera, Miguel Ángel Conde González, F. Martín","doi":"10.1109/ICARSC.2016.19","DOIUrl":"https://doi.org/10.1109/ICARSC.2016.19","url":null,"abstract":"Non-research spectators of robotic competitions perceive robot behaviors as deterministic. This perception plays a significant role in the acceptation of robots as social entities. This paper presents a motivational based architecture to generate natural autonomous robot behaviors that will help to improve the users' perception of robot's abilities. This proposal is based on Alderfer's simplification of Maslow's hierarchy of needs. A customized version of the ability test Speech Recognition & Audio Detection Test from the RoboCup competition has been implemented to illustrate how this architecture works. We discuss and analyze how the motivational variables affect the robot behaviors generated during the human-robot interaction. The preliminary tests show less deterministic robot behaviors than traditional approaches for robotic competitions.","PeriodicalId":293686,"journal":{"name":"International Conference on Autonomous Robot Systems and Competitions","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126878699","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}
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