Humanoid Robots Can Now Learn Actions by Watching Humans
jiqizhixin · x · 2026-07-17
This paper introduces **Human-as-Humanoid**: enabling humanoid robots to learn actions directly from synchronized first-person and third-person human videos, bypassing traditional teleoperation for data collection. Methodologically, they first aligned the design of a 60-DoF humanoid robot with human anatomy. Then, using phased inverse kinematics, they translated human motions into robot action sequences. The authors claim this data collection method is **4.8–7.2 times** faster than teleoperation. Crucially, the paper demonstrates that policies trained solely on these converted human videos can successfully generalize to real robots, achieving a "zero-shot" learning effect from human demonstrations.
More from Embodied
- Genspark launches a 2.95 mm AI voice recorder priced at $179 — Scobleizer · 2026-07-21
- Robot deployment data may look valuable, but narrow deployments create disconnected data islands — chris_j_paxton · 2026-07-21
- Mid-life crisis AI journey: dual 3090s + Qwen3.6-27b boost productivity 5x — Civil_Fee_7862 · 2026-07-21
- A talking humanoid robot demo at WAIC drew a “creepy” reaction — minchoi · 2026-07-21
- A creator says he appeared on NBC’s Today show to talk about robot fighting — chris_j_paxton · 2026-07-21
- WAIC 2026’s clearest embodied-AI signal: a server factory deployment plan targeting 10,000 units — 量子位 · 2026-07-21