Jensen Huang · Anthropic · Nvidia · Codex · GPT · Ars Technica
AI coding agents taught robots how to install GPUs and cut zip ties
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That glimpse into how AI can act in a fully autonomous way to automate robot training was made possible by a new agent harness framework—software that wraps around AI models to enable their use of various tools while also providing capabilities such as memory, context, constraint, and feedback loops.
Key facts
- The harness was tested with three different AI coding agents, including OpenAI’s Codex with GPT-5.5, Anthropic’s Claude Code with Opus 4.7, and Moonshot AI’s Kimi Code with Kimi K2.6
- During a whirlwind tour of South Korea in early June, Nvidia founder and CEO Jensen Huang also met with Hyundai Motor Executive Chair Chung Euisun to discuss scaling up the mass manufacturing
- A part of our NVIDIA GEAR lab now self-improves tirelessly overnight,” wrote Jim Fan, director of AI at NVIDIA, in a LinkedIn post
- Fan also jokingly described the goal of such AI-directed robot training, saying, “We all take a holiday and Jensen wouldn’t even notice,” in reference to Nvidia founder and CEO Jensen Huang
Summary
What happens when you give AI coding agents a lab full of robotic arms, some compute resources, and a “generous token budget” for teaching the robots various tasks? “A part of our NVIDIA GEAR lab now self-improves tirelessly overnight,” wrote Jim Fan, director of AI at NVIDIA, in a LinkedIn post. Fan also jokingly described the goal of such AI-directed robot training, saying, “We all take a holiday and Jensen wouldn’t even notice,” in reference to Nvidia founder and CEO Jensen Huang.