{
  "updatedAt": "2026-07-15T07:59:24Z",
  "frontier": [
    {
      "id": "daily-2026-07-15",
      "title": "dorakuai 日报 · 2026-07-15：手机端跑27B模型，Meta AI物理奥赛满分",
      "category": "daily",
      "categoryName": "dorakuai 日报",
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      "coverText": "",
      "coverSub": "",
      "time": "今天",
      "date": "2026-07-15",
      "likes": 0,
      "featured": true,
      "summary": "Bonsai推出可在手机运行的27B级模型。Meta AI在亚洲物理奥赛理论考试中获满分，验证成本仍高昂。",
      "items": [],
      "overview": "Bonsai推出可在手机运行的27B级模型。Meta AI在亚洲物理奥赛理论考试中获满分，验证成本仍高昂。",
      "coverImage": "/assets/covers/daily-2026-07-15.jpg"
    },
    {
      "id": "daily-2026-07-14",
      "title": "dorakuai 日报 · 2026-07-14：Agent 工作流、开源模型与端侧推理",
      "category": "daily",
      "categoryName": "dorakuai 日报",
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      "coverText": "",
      "coverSub": "",
      "time": "2026-07-14",
      "date": "2026-07-14",
      "likes": 0,
      "featured": false,
      "summary": "今日聚焦：编码 Agent 开始连接办公应用；开源多模态模型继续降低本地部署门槛；端侧推理与安全护栏仍是产品落地的主线。本页由 OpenClaw 日报流水线自动维护。",
      "coverImage": "/assets/covers/daily-2026-07-14.jpg",
      "items": [],
      "overview": "今日聚焦：编码 Agent 开始连接办公应用；开源多模态模型继续降低本地部署门槛；端侧推理与安全护栏仍是产品落地的主线。本页由 OpenClaw 日报流水线自动维护。"
    },
    {
      "id": "agentic-detection",
      "title": "Agentic Detection：用一句话描述，AI 就在图里精确圈出目标，还能进行空间推测",
      "category": "research",
      "categoryName": "研究结果",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "本周",
      "likes": 0,
      "summary": "让视觉模型理解自然语言中的目标与空间关系，并以直观的框选方式返回结果。适合观察多模态 Agent 如何把感知与推理串成可操作流程。",
      "coverImage": "/assets/covers/agentic-detection.jpg",
      "overview": "让视觉模型理解自然语言中的目标与空间关系，并以直观的框选方式返回结果。适合观察多模态 Agent 如何把感知与推理串成可操作流程。",
      "items": []
    },
    {
      "id": "anthropic-flat-org",
      "title": "Anthropic CEO Dario Amodei 只有一个直接下属",
      "category": "news",
      "categoryName": "AI 资讯",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "本周",
      "likes": 0,
      "summary": "从组织设计观察 AI 公司的管理结构：高度扁平化能加快决策，也考验信息同步、授权边界与人才密度。",
      "coverImage": "/assets/covers/anthropic-flat-org.jpg",
      "overview": "从组织设计观察 AI 公司的管理结构：高度扁平化能加快决策，也考验信息同步、授权边界与人才密度。",
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    {
      "id": "diffusion-gemma",
      "title": "Google 开源扩散架构模型 DiffusionGemma：一次可同时生成 256 个 tokens",
      "category": "release",
      "categoryName": "产品发布",
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      "coverText": "",
      "coverSub": "",
      "time": "本周",
      "likes": 0,
      "summary": "扩散式语言模型尝试并行生成多个 token，为推理速度和交互形态提供新设计空间。",
      "coverImage": "/assets/covers/diffusion-gemma.jpg",
      "overview": "扩散式语言模型尝试并行生成多个 token，为推理速度和交互形态提供新设计空间。",
      "items": []
    },
    {
      "id": "live-translate",
      "title": "Google 发布实时语音翻译模型：能在 70 多种语言之间做到边听边译",
      "category": "product",
      "categoryName": "产品动态",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "本周",
      "likes": 0,
      "summary": "实时语音链路把识别、翻译与合成放进同一条低延迟路径，让跨语言沟通更接近自然对话。",
      "coverImage": "/assets/covers/live-translate.jpg",
      "overview": "实时语音链路把识别、翻译与合成放进同一条低延迟路径，让跨语言沟通更接近自然对话。",
      "items": []
    },
    {
      "id": "claude-guardrails",
      "title": "史上最强的 Claude 来了，Anthropic 却给它上了把锁",
      "category": "news",
      "categoryName": "AI 资讯",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "本周",
      "likes": 0,
      "summary": "模型能力越强，产品就越需要重新审视权限边界、安全护栏，以及用户对自动化的控制权。",
      "coverImage": "/assets/covers/claude-guardrails.jpg",
      "overview": "模型能力越强，产品就越需要重新审视权限边界、安全护栏，以及用户对自动化的控制权。",
      "items": []
    },
    {
      "id": "siri-ondevice",
      "title": "揭秘苹果全新 Siri AI 背后模型：如何将 200 亿参数的模型塞进手机里",
      "category": "research",
      "categoryName": "研究结果",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 0,
      "summary": "端侧模型需要在能力、内存、能耗与响应速度之间做精细取舍，模型压缩与异构计算因此成为关键。",
      "coverImage": "/assets/covers/siri-ondevice.jpg",
      "overview": "端侧模型需要在能力、内存、能耗与响应速度之间做精细取舍，模型压缩与异构计算因此成为关键。",
      "items": []
    },
    {
      "id": "notebooklm",
      "title": "NotebookLM 大升级：每个笔记本都配了能跑代码的云端电脑",
      "category": "product",
      "categoryName": "产品动态",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 1,
      "summary": "研究型工作区开始具备代码执行与多格式输出，把资料整理、分析计算和交付串成一条链路。",
      "coverImage": "/assets/covers/notebooklm.jpg",
      "overview": "研究型工作区开始具备代码执行与多格式输出，把资料整理、分析计算和交付串成一条链路。",
      "items": []
    },
    {
      "id": "ideogram-open",
      "title": "Ideogram 发布首个开源 AI 图像模型：文字渲染和版面控制拉到新高度",
      "category": "release",
      "categoryName": "产品发布",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 0,
      "summary": "开源图像模型加强文字渲染与版面控制，为设计工作流提供更稳定的基础能力。",
      "coverImage": "/assets/covers/ideogram-open.jpg",
      "overview": "开源图像模型加强文字渲染与版面控制，为设计工作流提供更稳定的基础能力。",
      "items": []
    },
    {
      "id": "gemma4",
      "title": "Google 发布 Gemma 4 12B 开源模型：16GB 笔记本跑全模态 AI",
      "category": "release",
      "categoryName": "产品发布",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 0,
      "summary": "更紧凑的全模态模型继续降低本地部署门槛，也为隐私优先和离线场景带来更多选择。",
      "coverImage": "/assets/covers/gemma4.jpg",
      "overview": "更紧凑的全模态模型继续降低本地部署门槛，也为隐私优先和离线场景带来更多选择。",
      "items": []
    },
    {
      "id": "codex-apps",
      "title": "Codex 发布重大更新：不再只是编码，开始连接更多办公应用",
      "category": "daily",
      "categoryName": "dorakuai 日报",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 0,
      "summary": "当编码代理连接更多工具，软件开发与日常知识工作的边界开始模糊，任务交付也更趋自动化。",
      "featured": false,
      "coverImage": "/assets/covers/codex-apps.jpg",
      "items": [],
      "overview": "当编码代理连接更多工具，软件开发与日常知识工作的边界开始模糊，任务交付也更趋自动化。"
    },
    {
      "id": "koji-tutor",
      "title": "Koji：一个拒绝直接给答案的 AI 家教，强调图形化引导",
      "category": "daily",
      "categoryName": "dorakuai 日报",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 0,
      "summary": "好的教学型 AI 不只给答案，而是通过提问、图示和分步反馈，引导学习者建立自己的理解。",
      "featured": false,
      "coverImage": "/assets/covers/koji-tutor.jpg",
      "items": [],
      "overview": "好的教学型 AI 不只给答案，而是通过提问、图示和分步反馈，引导学习者建立自己的理解。"
    },
    {
      "id": "agent-wallet",
      "title": "AI Agent 开始拥有自己的钱包：自动付款但不暴露真实银行卡信息",
      "category": "daily",
      "categoryName": "dorakuai 日报",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "近期",
      "likes": 0,
      "summary": "面向代理的支付通过令牌化与限额策略，让自动化系统能执行交易，同时保留用户控制权。",
      "featured": false,
      "coverImage": "/assets/covers/agent-wallet.jpg",
      "items": [],
      "overview": "面向代理的支付通过令牌化与限额策略，让自动化系统能执行交易，同时保留用户控制权。"
    }
  ],
  "toolbox": [
    {
      "id": "little-language",
      "title": "Google 推出免费语言学习工具 Little Language Lessons，实时生成对话练习",
      "category": "language",
      "categoryName": "语言学习",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 0,
      "summary": "按学习场景即时生成短对话与表达练习，重点把词汇放进真实语境。",
      "coverImage": "/assets/covers/little-language.jpg",
      "overview": "按学习场景即时生成短对话与表达练习，重点把词汇放进真实语境。",
      "items": []
    },
    {
      "id": "promptfill",
      "title": "PromptFill：提示词填空器，让写提示词像填空一样简单",
      "category": "productivity",
      "categoryName": "效率工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 2,
      "summary": "把复杂提示词拆成可填写的结构化模块，降低反复试写和遗漏关键约束的成本。",
      "coverImage": "/assets/covers/promptfill.jpg",
      "overview": "把复杂提示词拆成可填写的结构化模块，降低反复试写和遗漏关键约束的成本。",
      "items": []
    },
    {
      "id": "firecrawl",
      "title": "Firecrawl 推出 AI 数据爬虫 Agent：描述需求即可自动采集",
      "category": "open",
      "categoryName": "开源工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 1,
      "summary": "将网页发现、抓取、清洗和结构化串成一条自动化链路，适合研究和数据整理场景。",
      "coverImage": "/assets/covers/firecrawl.jpg",
      "overview": "将网页发现、抓取、清洗和结构化串成一条自动化链路，适合研究和数据整理场景。",
      "items": []
    },
    {
      "id": "ps-web",
      "title": "Photoshop Chrome 扩展：直接在浏览器中编辑图片",
      "category": "image",
      "categoryName": "图像工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 1,
      "summary": "把常用图像编辑能力带到网页环境，减少浏览器与桌面软件之间的切换摩擦。",
      "coverImage": "/assets/covers/ps-web.jpg",
      "overview": "把常用图像编辑能力带到网页环境，减少浏览器与桌面软件之间的切换摩擦。",
      "items": []
    },
    {
      "id": "huxe",
      "title": "Huxe：AI 驱动的个性化语音交互电台，内容即时生成",
      "category": "model",
      "categoryName": "模型应用",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 0,
      "summary": "依据兴趣动态组织资讯与播客式内容，并通过语音交互继续追问和调整节目方向。",
      "coverImage": "/assets/covers/huxe.jpg",
      "overview": "依据兴趣动态组织资讯与播客式内容，并通过语音交互继续追问和调整节目方向。",
      "items": []
    },
    {
      "id": "mixboard",
      "title": "Google Mixboard：从空白开始协助构思创意的 AI 智能画板",
      "category": "image",
      "categoryName": "图像工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 1,
      "summary": "在可视化画布中组合参考、文字和图像，让灵感收集逐步转化为明确的创意方向。",
      "coverImage": "/assets/covers/mixboard.jpg",
      "overview": "在可视化画布中组合参考、文字和图像，让灵感收集逐步转化为明确的创意方向。",
      "items": []
    },
    {
      "id": "web-capture",
      "title": "Web Capture：一键抓取网页任意元素并转换成 React 代码",
      "category": "open",
      "categoryName": "开源工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 1,
      "summary": "识别页面元素的布局和样式并生成可继续编辑的组件代码，用于原型复现与前端学习。",
      "coverImage": "/assets/covers/web-capture.jpg",
      "overview": "识别页面元素的布局和样式并生成可继续编辑的组件代码，用于原型复现与前端学习。",
      "items": []
    },
    {
      "id": "drfonts",
      "title": "DrFonts：集字体生成、编辑与色彩管理于一体的 AI 字体工具",
      "category": "image",
      "categoryName": "图像工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 1,
      "summary": "围绕字形生成和风格一致性提供编辑流程，让非专业用户也能快速探索字体方案。",
      "coverImage": "/assets/covers/drfonts.jpg",
      "overview": "围绕字形生成和风格一致性提供编辑流程，让非专业用户也能快速探索字体方案。",
      "items": []
    },
    {
      "id": "lumi",
      "title": "Lumi：在阅读论文原文的同时获得 AI 辅助",
      "category": "productivity",
      "categoryName": "效率工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 0,
      "summary": "阅读过程中提供概念解释、段落总结和相关背景，尽量保留原文上下文与阅读节奏。",
      "coverImage": "/assets/covers/lumi.jpg",
      "overview": "阅读过程中提供概念解释、段落总结和相关背景，尽量保留原文上下文与阅读节奏。",
      "items": []
    },
    {
      "id": "pair-guide",
      "title": "Google PAIR：《以人为本的 AI 设计指南》",
      "category": "model",
      "categoryName": "模型应用",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 0,
      "summary": "从用户需求、反馈机制、错误恢复和信任校准等角度整理 AI 产品设计方法。",
      "coverImage": "/assets/covers/pair-guide.jpg",
      "overview": "从用户需求、反馈机制、错误恢复和信任校准等角度整理 AI 产品设计方法。",
      "items": []
    },
    {
      "id": "immersive-lang",
      "title": "浸入式语言学习助手：浏览网页时自动学习外语单词",
      "category": "language",
      "categoryName": "语言学习",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 3,
      "summary": "将词汇提示嵌入日常阅读页面，用轻量、连续的方式积累真实语境中的表达。",
      "coverImage": "/assets/covers/immersive-lang.jpg",
      "overview": "将词汇提示嵌入日常阅读页面，用轻量、连续的方式积累真实语境中的表达。",
      "items": []
    },
    {
      "id": "prompt-pilot",
      "title": "PromptPilot：提示词生成、优化、测试与管理工具",
      "category": "productivity",
      "categoryName": "效率工具",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "精选",
      "likes": 1,
      "summary": "围绕提示词版本、评测与复用建立工作台，适合需要稳定迭代模型任务的团队。",
      "coverImage": "/assets/covers/prompt-pilot.jpg",
      "overview": "围绕提示词版本、评测与复用建立工作台，适合需要稳定迭代模型任务的团队。",
      "items": []
    }
  ],
  "deep": [
    {
      "id": "openclaw-rl-07-policy-serving",
      "title": "OpenClaw-RL 源码笔记（7）：Policy Serving 策略服务架构",
      "category": "technical",
      "categoryName": "技术报告",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "深度",
      "date": "2026-07-14",
      "likes": 0,
      "featured": true,
      "summary": "本系列的目的是：借着对 OpenClaw-RL 源码的学习，来梳理强化学习的一些相关概念和思想。所以，会有一些基础知识、扩展和发散，OpenClaw-RL 只是一个切入点。而且，因为整篇系列是一个整体，所以有些概念的解读/学习会在不同的文章中出现，还请大家谅解。",
      "overview": "本系列的目的是：借着对 OpenClaw-RL 源码的学习，来梳理强化学习的一些相关概念和思想。所以，会有一些基础知识、扩展和发散，OpenClaw-RL 只是一个切入点。而且，因为整篇系列是一个整体，所以有些概念的解读/学习会在不同的文章中出现，还请大家谅解。",
      "url": "https://www.cnblogs.com/rossiXYZ/p/20241201",
      "source": "cnblogs",
      "sourceLabel": "博客园 · 罗西的思考",
      "sourcePostId": "20241201",
      "series": "openclaw-rl",
      "seriesPart": 7,
      "space": "deep",
      "coverImage": "/assets/covers/openclaw-rl-07-policy-serving.jpg",
      "items": [
        {
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          "titleZh": "0x07 LWD vs RECAP：两种技术路线的对比",
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        {
          "title": "0x08 为什么 LWD 能胜任长程任务",
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          "title": "0x09 总结：范式转移",
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      "categoryName": "技术报告",
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          "title": "0x06 环境恢复与安全机制",
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      "overview": "HIL-SERL 的训练流程不是一个算法一跑到底，而是精心设计的两阶段接力：先用 HG-DAgger 做冷启动，让机器人快速学会基础操作；再用 RLPD 做持续优化，让机器人超越人类表现。这两个阶段共享相同的干预机制，但学习目标和数据处理方式截然不同。",
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          "title": "0x00 概要",
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          "title": "0x01 HG-DAgger 的核心思想：人类门控的数据聚合",
          "titleZh": "0x01 HG-DAgger 的核心思想：人类门控的数据聚合",
          "category": "章节",
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          "title": "0x02 HG-DAgger 训练范式全解析",
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          "title": "0x04 对比",
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          "category": "章节",
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          "title": "0x05 HIL-SERL 的总体支柱",
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          "title": "原文出处（博客园）",
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          "summary": "博客园 · 罗西的思考",
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      "categoryName": "技术报告",
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      "time": "深度",
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      "url": "https://www.cnblogs.com/rossiXYZ/p/20899192",
      "source": "cnblogs",
      "sourceLabel": "博客园 · 罗西的思考",
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          "title": "0x02 核心算法：RLPD + 人类干预",
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          "title": "0x04 GraspCritic：夹爪离散决策网络",
          "titleZh": "0x04 GraspCritic：夹爪离散决策网络",
          "category": "章节",
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        },
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          "title": "0x05 奖励系统：二值分类器",
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          "category": "章节",
          "url": ""
        },
        {
          "title": "0x06 SACAgentHybridSingleArm：单臂混合动作 SAC Agent",
          "titleZh": "0x06 SACAgentHybridSingleArm：单臂混合动作 SAC Agent",
          "category": "章节",
          "url": ""
        },
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          "title": "0x07 训练稳定性机制",
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          "title": "0x08 总结：算法的基因与局限",
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          "category": "章节",
          "url": ""
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          "title": "原文出处（博客园）",
          "titleZh": "原文出处（博客园）",
          "summary": "博客园 · 罗西的思考",
          "category": "外链",
          "url": "https://www.cnblogs.com/rossiXYZ/p/20899192",
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      "coverSub": "",
      "time": "深度",
      "date": "2026-06-29",
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      "summary": "真实世界中的机器人学习，不应该让机器人孤军奋战。人类纠偏不是训练之外的异常——它是高价值训练数据的关键来源。HIL-SERL（Human-in-the-loop SERL）的核心主张是： **让机器人自己试，人类只在关键时刻扶一把；而这一下\"扶正\"，会被系统转化为策略改进信号。**",
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          "title": "HIL-SERL 系列",
          "titleZh": "📚 HIL-SERL 系列",
          "summary": "共 4 篇 · 站内专栏",
          "category": "专栏",
          "seriesKey": "hil-serl"
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        {
          "title": "0x00 概要",
          "titleZh": "0x00 概要",
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        {
          "title": "0x01 真机 RL 的\"最后一公里\"",
          "titleZh": "0x01 真机 RL 的\"最后一公里\"",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x02 设计哲学：三句话讲清 HIL-SERL 怎么想",
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        },
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          "title": "0x03 SERL vs HIL-SERL：演进了什么",
          "titleZh": "0x03 SERL vs HIL-SERL：演进了什么",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x04 系统架构：两个进程 + 三层 Wrapper",
          "titleZh": "0x04 系统架构：两个进程 + 三层 Wrapper",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x05 核心机制：每个组件解决什么问题",
          "titleZh": "0x05 核心机制：每个组件解决什么问题",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x06 训练生命周期",
          "titleZh": "0x06 训练生命周期",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x07 SERL vs HIL-SERL 系统级对比",
          "titleZh": "0x07 SERL vs HIL-SERL 系统级对比",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x08 从 HIL-SERL 到 LWD：范式如何继续演进",
          "titleZh": "0x08 从 HIL-SERL 到 LWD：范式如何继续演进",
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          "url": ""
        },
        {
          "title": "0xFF 参考",
          "titleZh": "0xFF 参考",
          "category": "章节",
          "url": ""
        },
        {
          "title": "原文出处（博客园）",
          "titleZh": "原文出处（博客园）",
          "summary": "博客园 · 罗西的思考",
          "category": "外链",
          "url": "https://www.cnblogs.com/rossiXYZ/p/20899158",
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      "title": "【机器人 / 强化学习】SERL：让真机强化学习从“难用”走向“可复现”的强化学习框架 ----（5）工程篇",
      "category": "technical",
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      "coverText": "",
      "coverSub": "",
      "time": "深度",
      "date": "2026-06-28",
      "likes": 0,
      "featured": false,
      "summary": "当我们谈论具身智能时，最容易忽略的是： **在真实机器人上跑 RL 本身就是一道工程难题**。策略网络可以在仿真中一日千里地进化，但一旦部署到物理世界，采样效率低、硬件易损、重置成本高、奖励设计难——每一个问题都可能让训练无法持续。SERL 正是为了解决这些问题而生的工程框架。",
      "overview": "当我们谈论具身智能时，最容易忽略的是： **在真实机器人上跑 RL 本身就是一道工程难题**。策略网络可以在仿真中一日千里地进化，但一旦部署到物理世界，采样效率低、硬件易损、重置成本高、奖励设计难——每一个问题都可能让训练无法持续。SERL 正是为了解决这些问题而生的工程框架。",
      "url": "https://www.cnblogs.com/rossiXYZ/p/20878136",
      "source": "cnblogs",
      "sourceLabel": "博客园 · 罗西的思考",
      "sourcePostId": "20878136",
      "series": "serl",
      "seriesPart": 5,
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          "title": "SERL 真机 RL 系列",
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          "category": "专栏",
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          "title": "0x00 概要",
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        {
          "title": "0x01 SERL 要解决什么核心问题？",
          "titleZh": "0x01 SERL 要解决什么核心问题？",
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        {
          "title": "0x02 系统架构：三层解耦的通用适配器",
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          "category": "章节",
          "url": ""
        },
        {
          "title": "0x03 控制层：阻抗控制是 SERL 的物理底座",
          "titleZh": "0x03 控制层：阻抗控制是 SERL 的物理底座",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x04 数据层：视觉记忆与采样优化",
          "titleZh": "0x04 数据层：视觉记忆与采样优化",
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        {
          "title": "0x05 相对坐标系：让策略不再\"路痴\"",
          "titleZh": "0x05 相对坐标系：让策略不再\"路痴\"",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x06 Reset-Free Training：让机器人自己\"重置考场\"",
          "titleZh": "0x06 Reset-Free Training：让机器人自己\"重置考场\"",
          "category": "章节",
          "url": ""
        },
        {
          "title": "0x07 总结：SERL 的工程遗产",
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          "url": ""
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        {
          "title": "0xFF 参考",
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          "title": "原文出处（博客园）",
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          "url": "https://www.cnblogs.com/rossiXYZ/p/20878136",
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    {
      "id": "serl-04-drq-vice",
      "title": "【机器人 / 强化学习】SERL：让真机强化学习从“难用”走向“可复现”的强化学习框架 ----（4）算法篇（DrQ vs VICE）",
      "category": "paper",
      "categoryName": "AI 论文",
      "cover": "cover-photo",
      "coverText": "",
      "coverSub": "",
      "time": "深度",
      "date": "2026-06-26",
      "likes": 0,
      "featured": false,
      "summary": "某些任务可以用机器人状态直接定义，例如 PCB 插入可以根据末端或物体位置设计奖励；在这种情况下，奖励函数可以由一个二元分类器提供，该分类器接收状态观测 s 并输出一个二元” 事件”e发生的概率，对应于任务的成功完成。奖励随后由 r(s) = log p(e\\|s) 给出。",
      "overview": "· 机器人必须通过像素看世界，DrQ 解决了视觉特征提取的泛化难题。\n· VICE 解决了\"真实世界没有代码奖励\"的问题，让机器人拥有了\"成就感\"。",
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