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backbone",[21,32,33],{},"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。",[21,35,36],{},"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。",[21,38,39],{},"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。",[16,41,42],{"id":42},"最近的發展",[21,44,45],{},"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 負責理解與規劃，快的動作專家負責即時控制。",[21,47,48,49,53,54,57,58,61,62,65],{},"2026 年的幾條主線包括：",[50,51,52],"strong",{},"效率","，七十億參數的模型推理速度離即時控制還差一個量級，因此出現大量蒸餾、量化和 token 剪枝的方法；",[50,55,56],{},"推理","，讓模型先「想一步」再動作；",[50,59,60],{},"資料擴張","，例如用數萬小時的人類第一人稱影片做預訓練，並發現靈巧度也存在 scaling law；以及",[50,63,64],{},"與世界模型結合","，讓模型一邊預測接下來會發生什麼，一邊決定要怎麼動。",[21,67,68],{},"VLA 還沒解決所有問題：泛化仍然脆弱，真機評估依然昂貴。但方向已經很清楚——把語言模型的成功配方，搬進物理世界。",{"title":70,"searchDepth":71,"depth":71,"links":72},"",2,[73,74,75],{"id":18,"depth":71,"text":19},{"id":29,"depth":71,"text":30},{"id":42,"depth":71,"text":42},"2026-08-19","把語言模型的成功配方搬進物理世界——VLA 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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。",[16,111,113],{"id":112},"為什麼要跟影片一起訓練",[50,114,112],{},[21,116,117],{},"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。",[21,119,120],{},"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。",[16,122,123],{"id":42},[50,124,42],{},[21,126,127,128,135,136,141,142,147],{},"2025 年 4 月的 ",[129,130,134],"a",{"href":131,"rel":132},"https://arxiv.org/abs/2504.02792",[133],"nofollow","Unified World Models"," 與 2026 年初的 ",[129,137,140],{"href":138,"rel":139},"https://arxiv.org/abs/2602.15922",[133],"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 ",[129,143,146],{"href":144,"rel":145},"https://www.dyna.co/dyna-2",[133],"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。",[21,149,150],{},"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。",[21,152,153],{},"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？",[155,156],"hr",{},[16,158,160],{"id":159},"延伸閱讀",[50,161,162],{},"🔗 延伸閱讀",[21,164,165],{},[50,166,167],{},"本篇主角",[169,170,171],"ul",{},[172,173,174,178],"li",{},[129,175,177],{"href":144,"rel":176},[133],"Dyna-2 技術報告","｜Dyna Robotics, 2026",[21,180,181],{},[50,182,183],{},"世界模型 × 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的人形機器人基礎模型",[21,246,247],{},[50,248,249],{},"技術基礎",[169,251,252],{},[172,253,254,259],{},[129,255,258],{"href":256,"rel":257},"https://arxiv.org/abs/2210.02747",[133],"Flow Matching","｜兩條路線目前都在用的生成方法",{"title":70,"searchDepth":71,"depth":71,"links":261},[262,263,264,265],{"id":103,"depth":71,"text":106},{"id":112,"depth":71,"text":112},{"id":42,"depth":71,"text":42},{"id":159,"depth":71,"text":162},"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":95,"description":267},"insights/dyna：讓機器人用「預測影片」學會動作的模型",[273,274,275],"dyna","world-action model","video 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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。",[16,303,305],{"id":304},"progressive-disclosure",[50,306,307],{},"Progressive Disclosure",[21,309,310,311,318],{},"Skill 的重要概念之一是 ",[129,312,315],{"href":313,"rel":314},"https://blog.aihao.tw/2026/05/20/llm-knowledge-base/",[133],[50,316,317],{},"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。",[16,320,322],{"id":321},"一般人也能使用skill",[50,323,324],{},"一般人也能使用Skill",[21,326,327,328,331,332,335],{},"更重要的是，",[50,329,330],{},"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",[50,333,334],{},"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 真正成為可持續累積能力的工作助手。",[16,337,339],{"id":338},"agent-skills-推薦",[50,340,341],{},"Agent Skills 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