Meta Muse installs PyTorch itself to drive a rover to a black ball
Told to find a black ball with only a 2D camera, Meta Muse installed PyTorch and Depth Anything V2 in its own VM, estimated and checked the distance, then drove an Earth Rover Mini+ to it without anyone writing that pipeline.
An AI agent was told to find a black ball. It only had a 2D camera, so it installed PyTorch and Depth Anything V2 inside its own VM, estimated the distance, sanity checked its own number, then drove the rover there. Nobody wrote that pipeline for it. Meta Muse assembled its own perception-to-action stack on an Earth Rover Mini+, and Alexandr Wang posted about it watching from the sidelines. Raj ran the experiment on @BitRobotNetwork hardware, which is the detail that makes it more than a demo. The Mini+ is a cheap consumer rover sitting on a sidewalk, not a lab platform behind a reservation system. An agent capable of writing its own perception layer paired with hardware anyone can buy is a very different distribution curve than agentic robotics on a $50,000 research arm. The RoboCap stack got sharper the same week. Grounded Superintelligence came out of stealth with an API doing metric 3D hand tracking and sub centimeter depth at manipulation range, integrated into LeRobot and Rerun, built specifically for the RoboCap suite. Raw egocentric video into policy ready data in minutes. One experiment by one person is not a trend, and agentic reliability across messy environments is still unmeasured. But the moment a machine installs its own tools to satisfy an instruction, the line between operating a robot and asking one stops being a metaphor.