A new framework from Nvidia, UC Berkeley, and Stanford systematically tests how well AI models can control robots through code. The findings: without human-designed abstractions, even top models fail, but methods like targeted test-time compute scaling closes the gap.
The article AI models fail at robot control without human-designed building blocks but agentic scaffolding closes the gap appeared first on The Decoder.