Synced · WeChatMay 10, 2026, 14:03 UTC+8

Ted Xiao Reviews Three Eras of Robot Learning, from RT-1/RT-2 to Scaling

具身智能来时路：谷歌RT1、2，SayCan作者Ted Xiao复盘机器人学习三大时代

Ted Xiao divides nearly a decade of robot learning into three eras: Google’s team trained RT-1 on 87,000 teleoperation trajectories, then adapted 5B to 55B VLMs into VLA policies for RT-2.

Why it matters: HKR-H/K/R all pass: a named Google robotics insider, concrete RT-1/RT-2 numbers, and strong embodied-AI resonance. It is retrospective commentary, not a launch, so it stays in the 72–77 featured band.

Read the original ↗Export Markdown

Take

The robotics boom did not start with a cleverer control algorithm; it started when Google admitted RL was operationally brutal. Ted Xiao’s concrete detail is the tell: the team entered “Code Yellowish,” stopped publishing for 18 months, hired nearly 10 operators, and collected 87,000 teleop trajectories. That gave RT-1 its stable base: 500 tasks and a 50M-parameter Transformer policy.

I don’t buy the clean “humanoid demos suddenly arrived” narrative. RT-2 adapting 5B-to-55B VLMs into VLA policies mattered, but it sat on the boring work: kitchen data, rewritten training infra, and behavior cloning moving from an 80% wall to 90–95%. Physical Intelligence and Gemini Robotics now package this as scaling. Fine. The ledger is still the same: high-quality real trajectories beat another round of sim-to-real mythology.

Source: Synced · WeChat · mp.weixin.qq.com
Published: May 10, 2026, 14:03 UTC+8 · May 10, 2026