NVIDIA GEAR实验室推出ENPIRE系统，首次实现物理世界自主研究。系统让8个Codex智能体控制8台机器人，配备GPU和token预算。安全方面采用硬运动极限切断和扭矩受限夹爪两层硬件保障，支持通宵无人运行。奖励函数通过视觉分类器离线固定并冻结，防止智能体作弊。实时监测机器人利用率（MRU）、token利用率（MTU）和GPU利用率，以Tokens-to-Success和Time-to-Success评估效率。ENPIRE自主完成扎带、整理细针、安装GPU等高精度任务，发现8机器人并行探索显著更快。系统将开源。
Jim Fan团队让8个机器人在真实世界自主研究，从安装GPU到发现物理扩展定律，这是具身智能第一次真正脱离人类监督探索物理任务，比任何虚拟环境的Agent实验都更接近AGI的物理锚点，做机器人的必须关注。
I made Physical AutoResearch sound simple (conceptually), but it took a village to pull off and lots of design thinking into the robot /loopcraft. The hardest part is everything we need to setup *before* pressing Enter. Here's a behind-the-scene tour:
1. Safety harness
Letting 8 robots run unattended overnight means safety has to be more than a hint in the system prompt. ENPIRE hardwires it in 2 layers: (1) hard kinematic limit that trips an immediate task failure and auto-resets as soon as a robot leaves its safety envelope, and (2) a torque-limited compliant gripper so a bad contact or misaligned insertion ends in a safe stall, instead of crushing the robot or the object at hand.
We make safety more conservative than usual so humans can sleep tight. In reality, we still need a few human operators to watch over the "robots of loving grace".
2. Definition of /done
An agent that can edit its own reward will game it for sure. ENPIRE fixes the goalposts before the fleet can move them. Here's the recipe:
Collect a few minutes of success & failure demos
-> Ask agent to write code using computer vision tools to classify success and measure against groundtruth
-> Agent hill-climbs on classifier until reliably good
-> This classifier becomes the real-time reward function that directly computes on sensor streams
-> *Freeze* the reward function before AutoResearch. It's sacred, enshrined in a Gym env that no one can touch.
3. System telemetry design
Robot-seconds is by far the scarcest resource, followed by GPU-seconds, and finally tokens. We instrument all three and surface them to ENPIRE for live resource awareness rather than letting it hill-climb in a vacuum.
We define:
- Mean Robot Utilization ("MRU"): the fraction of wall-clock time when the robot is actively executing an experiment. Otherwise the hardware is sitting idle and waiting for the next code commit.
- Mean Token Utilization ("MTU"): tokens consumed per minute, our proxy for how hard the agent is actually thinking. A low MTU means the agent is stalled, waiting on a robot rollout to finish instead of doing research.
- GPU utilization: fraction of wall-clock time when GPU is active.
... and evaluate on two budget-to-outcome metrics:
- Tokens-to-Success: token budget the fleet burns to complete /goal.
- Time-to-Success: wall-clock time to /goal
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 X 查看被引用的帖子
来源：Jim Fan · x.com