International Journal of Artificial Intelligence and Robotics Research最新文献

筛选
英文 中文
Vision-Guided Grasping Policy Learning from Demonstrations for Robotic Manipulators 从演示中学习机器人机械手的视觉引导抓取策略
International Journal of Artificial Intelligence and Robotics Research Pub Date : 2024-07-05 DOI: 10.1142/s2972335324500066
Lei Jiang, Feiyan Wang, Yueyue Liu
{"title":"Vision-Guided Grasping Policy Learning from Demonstrations for Robotic Manipulators","authors":"Lei Jiang, Feiyan Wang, Yueyue Liu","doi":"10.1142/s2972335324500066","DOIUrl":"https://doi.org/10.1142/s2972335324500066","url":null,"abstract":"","PeriodicalId":516715,"journal":{"name":"International Journal of Artificial Intelligence and Robotics Research","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675279","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
Inaugural Issue of the International Journal of Artificial Intelligence and Robotics Research (IJAIRR): The Emergence of an Interdisciplinary Nexus 国际人工智能与机器人研究期刊》(IJAIRR)创刊号:跨学科联系的出现
International Journal of Artificial Intelligence and Robotics Research Pub Date : 2024-03-01 DOI: 10.1142/s2972335324010026
Yu Sun, Dong Xu, Xiaorui Zhu
{"title":"Inaugural Issue of the International Journal of Artificial Intelligence and Robotics Research (IJAIRR): The Emergence of an Interdisciplinary Nexus","authors":"Yu Sun, Dong Xu, Xiaorui Zhu","doi":"10.1142/s2972335324010026","DOIUrl":"https://doi.org/10.1142/s2972335324010026","url":null,"abstract":"For the inaugural issue of the International Journal of Artificial Intelligence and Robotics Research (IJAIRR), I am honored to present an editorial that encapsulates the essence and ambition of this cutting-edge publication. IJAIRR emerges at a time when Artificial Intelligence and Robotics (AIR) are not merely technological novelties but fundamental drivers of progress across various scientific and practical domains. This journal aims to be at the forefront of documenting, analyzing, and guiding the interdisciplinary integration of AI, robotics, and fundamental sciences.","PeriodicalId":516715,"journal":{"name":"International Journal of Artificial Intelligence and Robotics Research","volume":"121 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089312","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
Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability 整合使用大型语言模型生成的机器人计划树以提高可靠性
International Journal of Artificial Intelligence and Robotics Research Pub Date : 2024-01-15 DOI: 10.1142/s2972335324500029
Md. Sadman Sakib, Yu Sun
{"title":"Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability","authors":"Md. Sadman Sakib, Yu Sun","doi":"10.1142/s2972335324500029","DOIUrl":"https://doi.org/10.1142/s2972335324500029","url":null,"abstract":"The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.","PeriodicalId":516715,"journal":{"name":"International Journal of Artificial Intelligence and Robotics Research","volume":" 56","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640608","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
0
×
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×
相关产品
×
本文献相关产品
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