基于强化学习的触觉传感用于主动点云采集、识别和定位

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Kevin Riou;Kaiwen Dong;Kevin Subrin;Patrick Le Callet
{"title":"基于强化学习的触觉传感用于主动点云采集、识别和定位","authors":"Kevin Riou;Kaiwen Dong;Kevin Subrin;Patrick Le Callet","doi":"10.1109/JSTSP.2024.3431203","DOIUrl":null,"url":null,"abstract":"Traditional passive point cloud acquisition systems, such as lidars or stereo cameras, can be impractical in real-life and industrial use cases. Firstly, some extreme environments may preclude the use of these sensors. Secondly, they capture information from the entire scene instead of focusing on areas relevant to the end task, such as object recognition and localization. In contrast, we propose to train a Reinforcement Learning (RL) agent with dual objectives: i) control a robot equipped with a tactile (or laser) sensor to iteratively collect a few relevant points from the scene, and ii) recognize and localize objects from the sparse point cloud which has been collected. The iterative point sampling strategy, referred to as an active sampling strategy, is jointly trained with the classifier and the pose estimator to ensure efficient exploration that focuses on areas relevant to the recognition task. To achive these two objectives, we introduce three RL reward terms: classification, exploration, and pose estimation rewards. These rewards serve the purpose of offering guidance and supervision in their respective domain, allowing us to delve into their individual impacts and contributions. We compare the proposed framework to both active sampling strategies and passive hard-coded sampling strategies coupled with state-of-the-art point cloud classifiers. Furthermore, we evaluate our framework in realistic scenarios, considering realistic and similar objects, as well as accounting for uncertainty in the object's position in the workspace.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":null,"pages":null},"PeriodicalIF":8.7000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reinforcement Learning Based Tactile Sensing for Active Point Cloud Acquisition, Recognition and Localization\",\"authors\":\"Kevin Riou;Kaiwen Dong;Kevin Subrin;Patrick Le Callet\",\"doi\":\"10.1109/JSTSP.2024.3431203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional passive point cloud acquisition systems, such as lidars or stereo cameras, can be impractical in real-life and industrial use cases. Firstly, some extreme environments may preclude the use of these sensors. Secondly, they capture information from the entire scene instead of focusing on areas relevant to the end task, such as object recognition and localization. In contrast, we propose to train a Reinforcement Learning (RL) agent with dual objectives: i) control a robot equipped with a tactile (or laser) sensor to iteratively collect a few relevant points from the scene, and ii) recognize and localize objects from the sparse point cloud which has been collected. The iterative point sampling strategy, referred to as an active sampling strategy, is jointly trained with the classifier and the pose estimator to ensure efficient exploration that focuses on areas relevant to the recognition task. To achive these two objectives, we introduce three RL reward terms: classification, exploration, and pose estimation rewards. These rewards serve the purpose of offering guidance and supervision in their respective domain, allowing us to delve into their individual impacts and contributions. We compare the proposed framework to both active sampling strategies and passive hard-coded sampling strategies coupled with state-of-the-art point cloud classifiers. Furthermore, we evaluate our framework in realistic scenarios, considering realistic and similar objects, as well as accounting for uncertainty in the object's position in the workspace.\",\"PeriodicalId\":13038,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.7000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10606195/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10606195/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

传统的被动点云采集系统,如激光雷达或立体摄像机,在现实生活和工业应用案例中可能并不实用。首先,在某些极端环境中可能无法使用这些传感器。其次,它们捕捉的是整个场景的信息,而不是与最终任务(如物体识别和定位)相关的区域。相比之下,我们建议训练一个具有双重目标的强化学习(RL)代理:i) 控制一个装有触觉(或激光)传感器的机器人,从场景中反复收集一些相关点;ii) 从收集到的稀疏点云中识别和定位物体。迭代点采样策略被称为主动采样策略,它与分类器和姿态估计器共同训练,以确保高效地探索与识别任务相关的区域。为了实现这两个目标,我们引入了三个 RL 奖励项:分类、探索和姿势估计奖励。这些奖励的目的是在各自的领域提供指导和监督,使我们能够深入研究它们各自的影响和贡献。我们将提议的框架与主动采样策略和被动硬编码采样策略以及最先进的点云分类器进行了比较。此外,我们还在现实场景中评估了我们的框架,考虑了现实和类似的物体,并考虑了物体在工作空间中位置的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement Learning Based Tactile Sensing for Active Point Cloud Acquisition, Recognition and Localization
Traditional passive point cloud acquisition systems, such as lidars or stereo cameras, can be impractical in real-life and industrial use cases. Firstly, some extreme environments may preclude the use of these sensors. Secondly, they capture information from the entire scene instead of focusing on areas relevant to the end task, such as object recognition and localization. In contrast, we propose to train a Reinforcement Learning (RL) agent with dual objectives: i) control a robot equipped with a tactile (or laser) sensor to iteratively collect a few relevant points from the scene, and ii) recognize and localize objects from the sparse point cloud which has been collected. The iterative point sampling strategy, referred to as an active sampling strategy, is jointly trained with the classifier and the pose estimator to ensure efficient exploration that focuses on areas relevant to the recognition task. To achive these two objectives, we introduce three RL reward terms: classification, exploration, and pose estimation rewards. These rewards serve the purpose of offering guidance and supervision in their respective domain, allowing us to delve into their individual impacts and contributions. We compare the proposed framework to both active sampling strategies and passive hard-coded sampling strategies coupled with state-of-the-art point cloud classifiers. Furthermore, we evaluate our framework in realistic scenarios, considering realistic and similar objects, as well as accounting for uncertainty in the object's position in the workspace.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信