{"title":"在人与机器人的物理合作过程中同时学习意图和偏好","authors":"Linda van der Spaa, Jens Kober, Michael Gienger","doi":"10.1007/s10514-024-10167-3","DOIUrl":null,"url":null,"abstract":"<div><p>The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"48 4-5","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10167-3.pdf","citationCount":"0","resultStr":"{\"title\":\"Simultaneously learning intentions and preferences during physical human-robot cooperation\",\"authors\":\"Linda van der Spaa, Jens Kober, Michael Gienger\",\"doi\":\"10.1007/s10514-024-10167-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.</p></div>\",\"PeriodicalId\":55409,\"journal\":{\"name\":\"Autonomous Robots\",\"volume\":\"48 4-5\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10514-024-10167-3.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Autonomous Robots\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10514-024-10167-3\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Robots","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10514-024-10167-3","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Simultaneously learning intentions and preferences during physical human-robot cooperation
The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.
期刊介绍:
Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development.
The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.