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DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-V4系列预览版论文中文翻译：1.6T参数MoE模型，支持百万Token上下文，引入CSA/HCA混合注意力、mHC超连接和Muon优化器。
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计算机视觉的惨痛教训（The Bitter Lesson of Computer Vision）
Vincent Sitzmann 关于计算机视觉未来的深度思考：传统的中间表示（如三维重建、分割掩码）将变得过时，计算机视觉的未来是作为端到端感知-动作循环的一部分。
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《VGGT》与《SwiftVGGT》深度解读：视觉几何基础模型的统一多任务范式
深度解读 CVPR 2025 Best Paper VGGT 及其后续工作 SwiftVGGT。VGGT 通过单次前向传播同时输出相机参数、深度图、点云和点跟踪，精度超越传统优化方法。SwiftVGGT 在此基础上通过单步 SVD 和内置回环检测，将大规模场景重建速度提升 3 倍，且无需任何训练。
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具身智能公司：本体-小脑路线 vs 大脑优先路线
A deep dive into the two major strategic paths in the embodied AI industry: Cerebellum-First vs. Brain-First, and the companies leading each camp.
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《LingBot-Map: 用于实时三维重建的几何上下文 Transformer》深度解读
LingBot-Map 是一种基于 Transformer 的前馈式三维基础模型，实现了超长序列的高精度、实时单目三维重建与位姿估计。
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π0.7：具有涌现能力的可引导通用机器人基础模型（中文翻译）
Physical Intelligence 最新论文 π0.7 中文全文翻译 - 一个5B参数的可引导通用机器人基础模型，展现出组合泛化能力，能够开箱即用地执行复杂灵巧任务，实现零样本跨具身迁移。
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π0.7：具有涌现能力的可引导通用机器人基础模型（中文全译）
本文是Physical Intelligence最新论文π0.7的完整中文翻译。π0.7是一个可引导的通用机器人基础模型，能够在开箱即用情况下执行高度灵巧的长周期任务，实现零样本跨具身迁移，并能通过语言指导学习新任务。
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《SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control》深度解读
NVIDIA 研究员将缩放定律（Scaling Law）应用到人形机器人全身控制，通过将模型从 1.2M 放大到 42M 参数、数据集扩大到 1 亿帧（700 小时），得到一个通用人形机器人基础控制器，支持多种输入接口（VR遥操作、视频、VLA模型）。
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π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities 深度解读
《π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities》深度解读 论文信息 - 作者：Bo Ai, Ali Amin, ..., Sergey Levine 等（Physical Intelligence 团队） - 机构：Physical Intel…
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论文解读：Look Before Acting - 增强视觉基础表示的视觉-语言-动作模型
本文解读了arXiv 2603.15618论文，该论文揭示了VLA模型中深层视觉敏感性下降的关键问题，并提出DeepVision-VLA框架，在模拟和真实任务上分别超出SOTA 9.0%和7.5%。
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From Evaluation to Closed-Loop Improvement: How Community Feedback Makes Robots Smarter
When we build a robot or train a new model, we face a fundamental question: how do we verify that it actually works safely and reliably across all the conditions it might encounter…
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XRollout Philosophy: The Art of Deliberate Practice
"Autobots, Roll Out!" — Optimus Prime The name "XRollout" carries dual meaning. It honors the iconic rallying cry of Optimus Prime from Transformers—a call to action, transformatio…
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Memory for Robotics: Enhancing Temporal Decision-Making
This article breaks down the MEM (Multi-scale Embodied Memory) approach from the Physical Intelligence (PI) research project. MEM enables robots to handle long-horizon tasks (up to…
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Why Language: A Human Brain Perspective on VLA
Starting from the human brain. Human conscious thinking primarily consists of five basic functions: understanding, decision-making, recollection, memory, and inhibition. These func…
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Community Credit System: The Duolingo Approach to Collaborative Robotics
In open-source robotics, we face a classic chicken-and-egg problem: 1. We need more contributors to collect data, fix bugs, write documentation, and share knowledge 2. But contribu…
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What We Do
XRollout was born from a simple but powerful belief: robotics should be open, accessible, and community-driven. We believe that the future of robot intelligence shouldn't be locked…
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Memory for Robotics: Enhancing Temporal Decision-Making
Breakdown of Multi-scale Embodied Memory (MEM) - enabling robots to handle long-horizon tasks by remembering what they've done.
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Community Credit System: The Duolingo Approach
Incentivizing contribution with a credit system where you earn by contributing and redeem for platform resources.
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From Evaluation to Closed-Loop Improvement
How community feedback closes the data loop and makes robots smarter in real-world scenarios.
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Why Language: A Human Brain Perspective on VLA
From prefrontal cortex vs basal ganglia to why both vision and language are indispensable in VLA.
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XRollout Philosophy: The Art of Deliberate Practice
Our complete learning philosophy — four pillars of robot acquisition and the hierarchical data pyramid.
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What We Do
Our mission - why we started XRollout and what we believe. Robotics should be open, accessible, and community-driven.
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SLAM: The Original Memory Theory
Simultaneous Localization and Mapping isn't just a robotics algorithm—it's a deep meditation on what memory actually is. When you build SLAM, you're actually building a memory syst…
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Memory as a Service: The Core Problem Revealed by π's MEM
Physical Intelligence's π project recently introduced MEM (Memory-based Manipulation), bringing memory architectures to the forefront of robot learning. MEM has two key innovations…
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CNN-SLAM vs 传统ORB-SLAM：基于深度学习的SLAM方案真的更好吗？
本文在公开TUM数据集上实证对比了CNN-SLAM (DINOv2+LoFTR) vs 传统ORB-SLAM，详细分析了两种方案的优缺点和适用场景。
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第一篇文章，测试一下，激动！！！
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