10月初刚公布的论文“Open X-Embodiment: Robotic Learning Datasets andRT-XModels”,除了谷歌research和deep mind,来自十几家美国、欧洲和亚洲的大学。 作者提出了一个开放的、大规模的机器人学习数据集,由全球21家机构筛选出来的。该数据集代表了多样的行为、机器人具身和环境,并能够学习泛化的机器人策略。 许多...
Page,Paper,Code, Data Prerequisite: RT-1, RT-21. 概述为了训练一个通用的机器人策略,Google推出了Open X-Embodiment数据集,包含在 22个机器人上采集的能够完成16万个任务的上百万条数据。并在原有RT-1和RT…
介绍openx-embodiment以及用RT-1和RT-2模型在该集成的数据集上进行训练的模型的一些实验及分析。, 视频播放量 283、弹幕量 2、点赞数 7、投硬币枚数 4、收藏人数 11、转发人数 1, 视频作者 kindlytree, 作者简介 和森AI信息技术分享平台,更多内容请参考网站: https://www.k
【Open X-Embodiment: Robotic Learning Datasets and RT-X Models】 文章链接:http://arxiv.org/abs/2310.08864 作者单位:亚利桑那州立大学;卡内基梅隆大学;哥伦比亚大学;Eth Z ̈ Urich;Flexiv Robotics;德国航天中心;Google Deep Mind;谷歌研究;伦敦帝国理工学院;Intrinsic LLC;意大利理工学院;韩国先进科学技术研究...
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a start...
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