因此本文提出使用VLMs:借助其强大的对图像的理解、推理等能力,结合机器人的数据(来自于RT1数据集)和原有的数据一起来对VLMs做co-fine-tuning,使其成为VLA(vision-language-action model),直接输出机器人的控制指令,实现实时的闭环控制。 作者通过6000次测试实验、证明了RT2的极强的泛化能力,涌现能力和推理能力。 2...
简单来说,只需要将action编码到text token中创建一个多模态的语句,即回复的指令中就包含了执行的动作。这与之前的方法(1. 结合VLM到robot policy;2. 设计一种新的vision-language-action架构)形成了鲜明的对比。本文将这种结构称为VLA模型,模型结构图如下: Vision-Language-Action Models 本文首先说明模型的总体结构...
介绍RT-2模型基于Vision Language Model用互联网级图片文本对数据和机器人数据进行co-finetue生成Vision Language Action model用户robotic control应用,实验验证了其在泛化能力和新任务能力上明显由于RT-1模型。, 视频播放量 465、弹幕量 0、点赞数 9、投硬币枚数 4、收藏
RT-2: Vision-Language-Action Models In the research paper “RT-2: Vision-Language-Action Models“, the AI division explain how “RT-2 can exhibit signs of chain-of-thought reasoning similarly to vision-language models.” This multi-stage semantic reasoning shows that RT-2 “is able to answ...
RT-2-Vision-Language-Action-Models-Transfer-Web-Knowledge-to-Robotic-Control 3.0万 77 23:49:38 App 【2025版】不愧是吴恩达教授!一口气讲透CNN、RNN、GAN、GNN、DQN、Transformer、LSTM等八大深度学习神经网络算法!简直不要太爽! 6.6万 216 01:35:08 App 【B站最新】吴恩达详细讲解Transformer工作原理,小...
Unlock the benefits of SysML/UML modeling for systems engineering and software development for embedded systems incl. small systems with limited resources.
This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliG
Hands-On Training1 i.MXRT 101x22 i.MXRT 102x21 i.MXRT 105x49 i.MXRT 106x47 i.MXRT 60017 Discussions Sort by: Views RT106X secure JTAG test and IDE debug RT106X secure JTAG test and IDE debug 1 Introduction Regarding the usage of RT10XX Secure JTAG, the nxp.com h...
reward,done,truncated,info=env.step(action)# for long horizon tasks, you can call env.advance_to_next_subtask() to advance to the next subtask; the environment might also autoadvance if env._elapsed_steps is larger than a thresholdnew_instruction=env.get_language_instruction()ifnew_...
AGI House(@agihouse_org)的推文引用了@DrJimFan的一句话,比喻了2024年的进展类似于扩散模型,暗示着一种混乱或不可预测的性质。推文暗示了对"AI"一词的疲劳或饱和感,并暗示着与人工智能相关事件在2024年年底达到重要影响或高潮。... 内容导读 AGI