英偉達GEAR實驗室聯合負責人Jim Fan昨日發布影片演示，宣布其團隊首次在物理世界中成功啟用AutoResearch系統。該系統基於團隊提出的ENPIRE編碼智能體框架，能夠將現實世界的機器人學習轉化為可控的優化過程，由智能體自主進行管理。
在實驗中，研究團隊為8個Codex智能體配備了多台機器人、GPU計算資源和充足的Token預算，並設定核心目標：在確保機器人安全運轉、不浪費計算資源的前提下，儘可能高效地完成任務。隨後，機器人開始自主探索——尋找視覺線索、重置場景、練習新技能、調整控制堆棧、在線閱讀論文、進行集體討論與反思，遇到瓶頸後直接在真實硬體上再次嘗試疊代。整個過程中，研究人員僅向Codex提供了接入物理世界的API接口，其餘所有操作均由AI自行發現和完成。
Today, we enable AutoResearch in the physical world for the first time! Introducing ENPIRE: we give 8 Codex agents a fleet of robots, an allocation of GPUs, and generous token budget. We set them free with a simple goal: solve the task as quickly as possible, keep the robots busy but stay safe, don't waste precious compute. Make no mistake. Then humans step aside and our watch begins. The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware. All we did is to give Codex an API to the world of atoms, and the rest is emergence. ENPIRE is able to solve high-precision tasks like tying zip-ties, organizing fine pins, and installing GPUs all by itself. We also discovered a new type of "physical scaling": 8 robots exploring in parallel improves significantly faster than fewer ones. A part of our NVIDIA GEAR lab now self-improves tirelessly over night. We just read the reports in the morning. /goal: we all take a holiday and Jensen wouldn't even notice ;) We will be open-sourcing everything, so you can host your self-running robot lab at home too! Deep dive in the thread:
在ENPIRE框架的加持下，機器人展現出令人驚嘆的高精度操作能力，可獨立完成系扎帶、整理釘子，甚至將顯卡準確插入電腦主機板等精細任務。Jim Fan表示，如今機器人能夠整夜不間斷地自我改進，研究人員只需在次日早晨查看訓練報告即可掌握進展，極大地解放了人力投入。
Jim Fan同時宣布，英偉達將開源這一技術。未來，科技愛好者也可以在家中自行託管屬於個人的自動運行機器人實驗室，讓前沿AI研究從專業機構走向更廣泛的開發者和發燒友群體。這一舉措被業內視為推動具身智能平民化的重要一步。
