找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection; Xuefeng Zhou,Hongmin Wu,Shuai Li Book‘‘‘‘‘‘‘‘ 2020 The E

[復(fù)制鏈接]
查看: 9430|回復(fù): 37
樓主
發(fā)表于 2025-3-21 17:12:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
編輯Xuefeng Zhou,Hongmin Wu,Shuai Li
視頻videohttp://file.papertrans.cn/668/667817/667817.mp4
概述Is the first book on robot introspection based on nonparametric Bayesian methods in a data-driven context, which can be easily integrated into various robotic systems.Introduces a fast, accurate, robo
圖書封面Titlebook: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection;  Xuefeng Zhou,Hongmin Wu,Shuai Li Book‘‘‘‘‘‘‘‘ 2020 The E
描述.This open access book focuses on?robot introspection,?which?has a direct impact on physical human–robot interaction?and?long-term autonomy,?and?which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,?the ability?to?reason,?solve their own?anomalies?and proactively?enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can?effectively?be modeled as a parametric?hidden Markov?model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the?hierarchical Dirichlet?process (HDP) on the standard HMM parameters,?known as the?Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and?allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods..This book is a?valuable?reference?resource for?researchers and designers in?the field?of robot learning and multimodal perception, as well as for senior undergrad
出版日期Book‘‘‘‘‘‘‘‘ 2020
關(guān)鍵詞Collaborative Robot Introspection; Nonparametric Bayesian Inference; Anomaly Monitoring and Diagnosis;
版次1
doihttps://doi.org/10.1007/978-981-15-6263-1
isbn_softcover978-981-15-6265-5
isbn_ebook978-981-15-6263-1
copyrightThe Editor(s) (if applicable) and The Author(s) 2020
The information of publication is updating

書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection影響因子(影響力)




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection影響因子(影響力)學(xué)科排名




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection網(wǎng)絡(luò)公開度




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection被引頻次




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection被引頻次學(xué)科排名




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection年度引用




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection年度引用學(xué)科排名




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection讀者反饋




書目名稱Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 20:47:05 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:17:03 | 只看該作者
地板
發(fā)表于 2025-3-22 04:55:11 | 只看該作者
5#
發(fā)表于 2025-3-22 09:57:57 | 只看該作者
6#
發(fā)表于 2025-3-22 14:40:30 | 只看該作者
7#
發(fā)表于 2025-3-22 19:11:00 | 只看該作者
8#
發(fā)表于 2025-3-22 22:44:38 | 只看該作者
Learning Policy for Robot Anomaly Recovery Based on Robot Introspection,quired. Then, we heuristically generate a set of synthetic demonstrations for augmenting the learning by appending a multivariate Gaussian noise distribution with mean equal to zeros and covariance equal to ones. Such that the corresponding introspective capabilities are learned and updated when ano
9#
發(fā)表于 2025-3-23 04:39:04 | 只看該作者
10#
發(fā)表于 2025-3-23 05:32:33 | 只看該作者
Xuefeng Zhou,Hongmin Wu,Juan Rojas,Zhihao Xu,Shuai Lin an area beyond that frontier, while subsuming it. Hence it is not the case that the concept is absent in the thought of Aurobindo, rather, it does not occupy as well-defined a place as it does in the thought of some other modern Hindu thinkers, and therefore has to be teased out, though not artificially but certainly deliberately.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-6 23:37
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
鄂州市| 融水| 平果县| 盐山县| 称多县| 和平县| 昂仁县| 铜陵市| 桂阳县| 深州市| 长白| 共和县| 昂仁县| 白山市| 金坛市| 沁源县| 惠水县| 济阳县| 尉犁县| 东兰县| 昔阳县| 大方县| 杭锦旗| 古蔺县| 长海县| 会同县| 通道| 洛川县| 平乡县| 睢宁县| 洛川县| 香格里拉县| 龙南县| 丹寨县| 澎湖县| 亚东县| 卢氏县| 东平县| 莲花县| 道孚县| 乐昌市|