Google released RT‑2, which I described as the GPT‑2 of Robotics. The paper “RT‑2: Vision‑Language‑Action Models Transfer Web Knowledge to Robotic Control” provides the technical details.
RT‑2 is the GPT‑2 of Robotics.
I noted that 99 % of robots are still “stuck in the 1990s” because development has focused on hardware bodies while neglecting the brain/learning component.
This realization pushed me to shift from hard‑coded task‑specific programming toward a Physical AI paradigm, training a single foundation‑model brain on massive video corpora so it can control heterogeneous robot bodies in real‑time.
I highlighted the 2026 trend of scaling via video, where robots “watch” millions of hours of human and animal videos to learn physics before deployment.
I reflected on the podcast from 2026‑02‑11 where Tony Zhao warned about the "Fragmentation Trap" in robotics.
The bottleneck is identified as a data gap rather than algorithmic limits.
The "recipe" for robot manipulation is finally stabilizing in academia.
We must scale physical data the same way text data was scaled for LLMs to reach consumer‑grade robots.
The era of "building from scratch" is ending; the era of the Generalist Robot is beginning.
I am shifting focus to a unified architecture and large‑scale data pipelines to overcome the fragmentation trap.
I expanded my view of robot adaptability by noting Skild AI’s body‑agnostic brain that discovers its morphology in a 500 ms discovery phase.
I recorded that the discovery phase lasts 500 ms.
I noted that the robot can re‑calculate locomotion in milliseconds after a limb is removed.
I asserted that GOFE is aimed at enabling fully general‑purpose robots, signaling a move away from bespoke, task‑specific robotic solutions.
I recorded the timestamp of this statement as 2026‑03‑18 02:33 UTC.
What does GOFE stand for and how does it relate to achieving fully general‑purpose robots?