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The closest I came to the future in 2021 was a waiting list I never joined. I was new to the field then, and a colleague told me about a model you could reach through an API that would, he said, answer almost any question you put to it. There was a queue for access; he may have signed up, but I told myself it would take a while and never got around to it. I did skim the announcement post, and I remember the thought that crossed my mind before I filed it away: once the masses find out about this, it is going to be huge. Then I lost interest and went looking at other things. That model was GPT-3, and what stays with me now is not the model but the shrug: I saw a corner of what was coming, labelled it correctly, and still felt nothing move. It would be nearly two years before I understood that the ground had shifted anyway, and that the thing which shifted first was not the machine but what a small number of people had let themselves believe was possible.

In 1988 the roboticist Hans Moravec noticed something embarrassing about artificial intelligence: it is “comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”¹ Nearly four decades later that paradox still defines robotics, but the way the field attacks it has been completely rebuilt, twice. This post traces that rebuild: how hand-engineered control gave way to reinforcement learning, why reinforcement learning then lost the manipulation battle to a much older idea (copying people), and how the last four years fused everything into robot foundation models that look strikingly like their language-model cousins. The goal is a self-contained tour: enough math to see why each transition happened, real numbers from the papers that drove it, and a running experiment you can reproduce.

Table of Contents

Table of Contents