ENPIRE：Codex智能体自主操控机器人完成精细任务
NVIDIA让8个AI智能体自己控制机器人干活，还能绑扎带装显卡，而且要开源，你可以在家搭机器人实验室了！
ENPIRE赋予8个Codex智能体机器人集群和GPU资源，自主完成高精度物理任务。系统能独立绑扎带、整理细针、安装GPU。
实验显示8台机器人并行探索比少量效率显著提升。NVIDIA GEAR实验室已实现机器人彻夜自主改进。
团队计划开源全部代码。
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:
This report is categorized under AITOP's "Products" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include ENPIRE, Codex, NVIDIA, agents.
Source attribution: curated from Jim Fan (@jimfan). Cross-source corroboration: 11 independent sources have covered this development (including 宝玉, IT之家, AlphaSignal), confirming industry-wide signal strength.
