Open X-Embodiment: Robotic Learning Datasets and RT-X Models 阅读笔记 曹玥 36 人赞同了该文章 Page,Paper,Code, DataPrerequisite: RT-1, RT-2 1. 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...
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...
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Revisiting Deep Learning Models for Tabular Data (NeurIPS 2021) Important Check out the new tabular DL model: TabM 📜 arXiv 📦 Python package 📚 Other tabular DL projects This is the official implementation of the paper "Revisiting Deep Learning Models for Tabular Data". TL;DR In one se...
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...
Prerequisite I have searched Issues and Discussions but cannot get the expected help. The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmpose). Environment # Name Version Build Channel _libgcc_mutex 0.1 main...
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3.3.1 着色模型对比 Comparison of Shader Models 下表3.1比较了各种着色模型的能力。在这个表格中,VS”代表顶点着色器和“PS”代表像素着色器(而着色模型4.0 引入了几何着色器,其能力与顶点着色相似)。如果没有出现“VS”和“PS”,那么该行适用于顶点和像素着色器。
Preemption Models有详细介绍,其中常用的有Desktop、Low-Latency Desktop和RT。 Desktop模式在内核总增加了更多的主动抢占,同时还在系统调用返回和中断返回中增加了抢占点。 Low-Latency Desktop模式让内核在所有非critical section可抢占,并且在禁止抢占退出的时候增加了抢占点。