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則可以把一套工作方法、規則、參考資料與工具封裝成可重複使用的「技能」，讓 AI 在遇到適合的任務時自行載入並執行。這使 AI 不只是回答問題，而是能依照既定流程完成較複雜、重複性高的工作，也讓個人或組織能逐步累積自己的 AI 工作能力。對企業來說，同一套技能也能讓不同人使用相近流程，降低重複教學與操作差異。",[16,37,39],{"id":38},"progressive-disclosure",[20,40,41],{},"Progressive Disclosure",[24,43,44,45,52],{},"Skill 的重要概念之一是 ",[28,46,49],{"href":47,"rel":48},"https://blog.aihao.tw/2026/05/20/llm-knowledge-base/",[32],[20,50,51],{},"Progressive Disclosure（漸進式披露）","。AI 不需要一開始就讀取所有 Skills 的完整內容，而是先知道目前有哪些技能，再根據任務判斷需要哪一項，之後才載入相關指令、文件、範例與其他資源。這種方式可以減少 Context 與 Token 的浪費，也避免一次塞入過多資訊影響模型判斷。當 Skills 數量愈來愈多時，這種「需要時才載入」的設計，也讓 Agent 更容易擴充不同領域的專業能力。",[16,54,56],{"id":55},"一般人也能使用skill",[20,57,58],{},"一般人也能使用Skill",[24,60,61,62,65,66,69],{},"更重要的是，",[20,63,64],{},"Skill 不只適合開發者","。一般使用者也能把經常重複的工作整理成 Skill，例如依公司格式製作會議紀錄、按照固定規則整理研究論文、修改英文履歷、產生每週工作報告，甚至整理旅遊規劃或個人學習流程。使用者主要需要把操作步驟、規則、範例與參考資料整理清楚，不一定要會複雜程式設計，也可以直接使用別人建立好的 Skill，再依自己的需求調整內容。未來使用 AI 的能力可能不只是「會下 Prompt」，而是",[20,67,68],{},"懂得把自己的知識與工作流程整理成 AI 可以反覆使用的 Skill","，讓 AI 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已不只是開發者的工具，而是逐漸延伸到研究、寫作與日常辦公等一般使用情境。",{"title":114,"searchDepth":115,"depth":115,"links":116},"",2,[117,118,119,120],{"id":18,"depth":115,"text":22},{"id":38,"depth":115,"text":41},{"id":55,"depth":115,"text":58},{"id":72,"depth":115,"text":75},"2026-08-31","Agent Skills 將工作流程、規則與領域知識封裝成可重複使用的 AI 能力，使模型能透過「漸進式披露」機制執行複雜任務，同時避免佔用過多上下文空間。",false,"md",{},true,"/insights/agent-skills-ai",{"title":5,"description":122},"insights/agent-skills：讓-ai-學會可重複使用的專業技能",[22,41,131,97,108],"Anthropic","agent-skills-progressive-disclosure","1uwbvK8pSat--FHnc5-gHilQqFjFBktdgEq7Vtc1sWE",[135,319,391],{"id":136,"title":137,"affiliation":138,"audiences":139,"author":140,"body":141,"date":307,"description":308,"draft":123,"extension":124,"featured":123,"meta":309,"navigation":126,"path":310,"seo":311,"stem":312,"tags":313,"updatedAt":307,"urlname":317,"__hash__":318},"insights/insights/dyna：讓機器人用「預測影片」學會動作的模型.md","Dyna：讓機器人用「預測影片」學會動作的模型","國立清華大學資訊工程學系",[8,9,10],"張宛琪",{"type":13,"value":142,"toc":301},[143,149,152,157,160,163,168,189,192,195,198,204,209,220,225,247,252,286,291],[16,144,146],{"id":145},"什麼是-dyna",[20,147,148],{},"什麼是 Dyna",[24,150,151],{},"Dyna 是 world-action model（WAM）路線的代表。跟 VLA 不同，它不是「看懂語言再生動作」，而是用一個影片擴散模型同時預測「接下來的畫面會怎麼變」和「機器人下一步該怎麼動」，動作只是接在影片骨幹旁的一條淺分支。",[16,153,155],{"id":154},"為什麼要跟影片一起訓練",[20,156,154],{},[24,158,159],{},"Dyna 的邏輯是：機器人缺的不是語言常識，而是對物理世界如何演變的直覺——手碰到東西會怎樣、繩子怎麼打結。這種知識，語言模型學不到，但人類第一人稱影片裡到處都是，而且量近乎無限，遠比遙控機械手臂錄示範便宜。他們從影片萃取手部姿態當成偽動作標籤來訓練。",[24,161,162],{},"實驗發現：只練動作、不生成影片的模型，換一個沒看過的機器人平台幾乎學不會遷移；一旦加上「同時預測未來影片」，零樣本表現大幅超越純動作訓練。甚至只是多餵沒有動作標籤、純粹拿來學影片生成的人類影片，機器人任務表現也會持續變好——影片本身成了新的擴增資料來源。",[16,164,166],{"id":165},"最近的發展",[20,167,165],{},[24,169,170,171,176,177,182,183,188],{},"2025 年 4 月的 ",[28,172,175],{"href":173,"rel":174},"https://arxiv.org/abs/2504.02792",[32],"Unified World Models"," 與 2026 年初的 ",[28,178,181],{"href":179,"rel":180},"https://arxiv.org/abs/2602.15922",[32],"DreamZero"," 等工作，把世界模型與動作合成一個模型。2026 年 8 月，Dyna Robotics 發布 ",[28,184,187],{"href":185,"rel":186},"https://www.dyna.co/dyna-2",[32],"Dyna-2","，預訓練規模衝上百萬小時人類第一人稱影片，首次驗證：人類資料規模擴大呈冪律改善，且首次觀察到「人到機器人的遷移擴展律」——模型沒看過任何機器人資料，光靠擴增人類影片，機器人任務的離線預測誤差就同步下降。實機上，僅用幾小時機器人示範做後訓練，百萬小時預訓練模型在雙臂、靈巧手、半人形平台都表現最佳，甚至只用十分鐘遙控資料就學會開瓶蓋，並展現對燈光變化、視覺遮蔽、持續干擾的高度穩健性。",[24,190,191],{},"Dyna 路線目前仍是少數團隊在探索，訓練成本更高，能否持續擴展到千萬小時規模也未知，但它給出了與 VLA 不同的答案：與其借語言常識，不如直接讓模型學會預測世界如何變化。",[24,193,194],{},"最耐人尋味的，是一個反直覺的細節：多餵純影片資料，對機器人任務持續有幫助，但對預測人類自己的動作幾乎沒用、甚至略微變差。同一批資料，對「跨到別的身體」有效，對「留在原本的身體」無效。這暗示影片預測學到的不是「怎麼動」，而是「世界會怎麼變」——人的手和夾爪長得完全不同，但杯子推倒會滾、繩子拉緊會繃直，這些規律對誰都一樣。若這條路走得通，機器人的資料瓶頸就從「有多少人願意去操控手臂」，變成「有多少人的生活被記錄下來」。而那也會帶出一個更難回答的問題：誰的生活，誰同意的？",[196,197],"hr",{},[16,199,201],{"id":200},"延伸閱讀",[20,202,203],{},"🔗 延伸閱讀",[24,205,206],{},[20,207,208],{},"本篇主角",[210,211,212],"ul",{},[213,214,215,219],"li",{},[28,216,218],{"href":185,"rel":217},[32],"Dyna-2 技術報告","｜Dyna Robotics, 2026",[24,221,222],{},[20,223,224],{},"世界模型 × 動作",[210,226,227,233,239],{},[213,228,229,232],{},[28,230,175],{"href":173,"rel":231},[32],"｜影片與動作擴散的耦合預訓練",[213,234,235,238],{},[28,236,181],{"href":179,"rel":237},[32],"｜World Action Models are Zero-shot Policies",[213,240,241,246],{},[28,242,245],{"href":243,"rel":244},"https://arxiv.org/abs/2602.16710",[32],"EgoScale","｜以第一人稱人類影片擴展靈巧操作",[24,248,249],{},[20,250,251],{},"對照組：VLA 路線",[210,253,254,262,270,278],{},[213,255,256,261],{},[28,257,260],{"href":258,"rel":259},"https://arxiv.org/abs/2307.15818",[32],"RT-2","｜開啟這條路線的起點",[213,263,264,269],{},[28,265,268],{"href":266,"rel":267},"https://arxiv.org/abs/2406.09246",[32],"OpenVLA","｜開源實作，適合入門",[213,271,272,277],{},[28,273,276],{"href":274,"rel":275},"https://arxiv.org/abs/2410.24164",[32],"π0","｜以 flow matching 生成連續動作",[213,279,280,285],{},[28,281,284],{"href":282,"rel":283},"https://arxiv.org/abs/2503.14734",[32],"GR00T N1","｜NVIDIA 的人形機器人基礎模型",[24,287,288],{},[20,289,290],{},"技術基礎",[210,292,293],{},[213,294,295,300],{},[28,296,299],{"href":297,"rel":298},"https://arxiv.org/abs/2210.02747",[32],"Flow Matching","｜兩條路線目前都在用的生成方法",{"title":114,"searchDepth":115,"depth":115,"links":302},[303,304,305,306],{"id":145,"depth":115,"text":148},{"id":154,"depth":115,"text":154},{"id":165,"depth":115,"text":165},{"id":200,"depth":115,"text":203},"2026-09-14","Dyna 屬於「世界-動作模型」路線，透過影片擴散模型同時預測未來畫面演變與機器人動作，利用龐大的人類第一人稱影片萃取手部姿態進行預訓練，打破傳統仰賴遙控機械手臂的資料瓶頸，讓模型學會物理世界變化的規律，僅需極少量的後訓練微調即可實現高度穩健且跨平台的機器人操控。",{},"/insights/dyna",{"title":137,"description":308},"insights/dyna：讓機器人用「預測影片」學會動作的模型",[314,315,316],"dyna","world-action model","video 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資訊工程學系",[8,10,9],"李達安",{"type":13,"value":398,"toc":449},[399,403,406,409,413,416,419,422,424,427,446],[16,400,402],{"id":401},"什麼是-vla","什麼是 VLA",[24,404,405],{},"VLA 是 Vision-Language-Action 的縮寫。它的輸入是攝影機畫面加上一句自然語言指令，例如「把番茄醬放進籃子裡」；輸出則是機器人下一步該怎麼動——手臂的關節角度、夾爪的開合。",[24,407,408],{},"過去的機器人，一個任務要工程師手刻一套程式，或訓練一個專用模型，換個場景就得重來。VLA 想做的是「一個模型、多種任務、多種機器人」，成為機器人的通用大腦。",[16,410,412],{"id":411},"為什麼要用-vlm-當-backbone","為什麼要用 VLM 當 backbone",[24,414,415],{},"關鍵在資料量。網路上有數兆字的文本與數十億張圖片，但機器人操作資料得靠真人遙控機械手臂一筆一筆錄下來，數量少了好幾個量級。從零開始訓練，模型只會死背示範動作，換一個沒看過的杯子就失敗。",[24,417,418],{},"而視覺語言模型（VLM）已經在網路規模的資料上，學會了「番茄醬長什麼樣」「紅色的球在左邊」「杯子要抓把手」這類視覺常識與語言理解。VLA 的做法就是直接接手這顆大腦，在後面加上一個「動作專家」，把動作當成另一種要生成的語言來預測。",[24,420,421],{},"這樣一來，機器人的泛化能力是從網路資料繼承來的，而不是從稀少的機器人資料硬學。面對沒見過的物體、沒聽過的講法，才有機會做對。",[16,423,165],{"id":165},[24,425,426],{},"2023 年的 RT-2 首次證明這條路可行，2024 年的 OpenVLA 開源讓學術界也能開始使用。2025 年之後開始走向產品化：Physical Intelligence 的 π0 / π0.5 用 flow matching 生成連續且高頻的動作；NVIDIA GR00T 與 Google Gemini Robotics 則採「快慢雙系統」——慢的 VLM 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