Import AI 463: Self-improving robots; a 10k Chinese GPU cluster; and an elegiac essay for the human era

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NVIDIA sets up a crude self-improvement loop for real world robotics: …What if you could take the best ideas from AI agents and put them into the real world?... Researchers with NVIDIA have developed ENPIRE, software to get physical robotics to go through the same kind of autonomous experimentation and execution loop that AI agents go through. The research gives us a taste of what it might look like for a superintelligence to attempt to use robots to instantiate itself in the physical world - though as with all things in robotics, the current examples are suggestive at best. What ENPIRE is: The software is “a harness framework for coding agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with single or multiple physical robots operating in parallel, and an Evolution module (E) in which coding agents analyze logs, consult literature, improve training infrastructure and algorithm code to address failure modes”. ENPIRE works the same way that coding agents work - a scaffold supervises some physical robots which are asked to complete tasks. The robots try to complete the tasks and attempt different strategies for completing stuff, trying and failing and learning. The system both evaluates their success and also resets itself when they fail. “This closed-loop system transforms real-world robot learning into a controllable optimization procedure that agents can manage, thus minimizing human effort while allowing fair ablations across training recipes and agent variants.” Two of the key ingredients for making this work are an automatic evaluation system to help score “the outcome of each trial without human judgement”, as well as an automatic reset system which “returns the scene to a fresh initial state for the next trial”. (Both of these are tasks which have historically required lots of human effort, and it’s likely that more complicated tasks would also require human effort for evaluation and resets, so in some sense the complexity of tasks a system like this can attack is also defined by our ability to automatically evaluate and reset the system). Hardware details: “Each station comprises two YAM (Yet Another Manipulator) arms from I2RT in a fixed bimanual configuration, a set of cameras, and a single workstation that runs the FastAPI server, policy inference, and the station’s agent.” Each workstation is running a NVIDIA RTX 5090. It works well (on some simple tasks) : “Frontier coding agents can autonomously develop a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks in the real world, such as PushT, organizing pins into a pin box, and using a cutter to cut a zip tie,” the authors write. An additional task they test out on is seeing how well the robot can insert GPUs into a motherboard. Some AI systems are better than others, but many AI systems are always better than fewer: GPT-5.5 within Codex and Opus 4.7 within Claude Code trade off with one another for best performance, while Kimi-2.6 lags. There are also compelling returns to scale for agents, with larger numbers of agents (e.g., 8) arriving at higher scoring solutions sooner than others - and sometimes multi-agent setups yield a higher absolute score than a single agent setup, likely due to exploring more of the potential solution space. Challenges remain for fleet instrumentation: “Coding agents do not fully utilize robot resources when they are reading logs, writing code, debugging, or waiting for the language-model backbone. As the number of robots scales, MRU decreases while GPU active utilization increases,” they write. In other words, there are some infrastructure challenges with adding multiple robot agents so things don’t naturally parallelize. Read more: ENPIRE: Agentic Robot Policy Self-Improvement in the Real World (NVIDIA research website) . Read more : ENPIRE: Agentic Robot Policy Self-Improvement in the Real World (arXiv) . ***

They looked through the monitors at the chamber. The container for the reaction did not look like anything designed by engineering processes, but was rather a twisting oddly shaped donut of metal, the shapes fluid and unintuitive; a stellarator.

The design of the thing had come down to them from an overmind after a multi-day thinking job. The fabrication had taken place at a machine syndicate; then the parts arrived and were assembled by some bipeds subcontracted by the humans from another syndicate.

For the ribbon-cutting ceremony, a few humans gathered and posed for some photographs and some footage, taken by cam-drones and a few humans with smartphones. The robots stood out of shot. People had gotten used to this - there was an adolescence where people took photos with the humans and the robots but public sentiment always spiked downward upon exposure to this and eventually it was simpler to shoot with the robot partners out of frame, much like how human paparazzi tried to tastefully avoid capturing the security guards of their celebrity targets.