How robots learn: A brief, contemporary history

Companies and investors put $6.1 billion into humanoid robots in 2025, 4x 2024, and MIT Technology Review attributes the surge to a shift in how robots learn. The piece highlights two mechanisms: around 2015, simulation plus reward signals enabled millions of trial-and-error runs; after ChatGPT in 2022, robotics models took images, sensors, and joint states to predict dozens of motor commands per second. The key change is data-driven learning over hand-written rules; the provided text is truncated, so later examples are not fully disclosed.

Why it matters: HKR-H/K/R all pass: the $6.1B and 4x funding jump provide the hook, and the piece maps the shift from sim+RL to multimodal action models. It stays in the lower featured band because this is commentary rather than a new release, and the excerpt is truncated on company-level detail