Reward AI’s OM-1 Model: Why Human Motion Capture Is Accelerating Robot Training

US-based Reward AI released videos of its Omnibody Model 1 (OM-1) foundational robot model, which learns complex tasks solely from human movements captured by a wearable device and can pick up new tasks from less than 30 minutes of human demonstration. The videos, presented at 1x speed, show robots packing smartphones, mixing cocktails, folding laundry, and unplugging Ethernet cables, and OM-1 is designed for cross-robot compatibility from industrial arms to humanoids. Reward AI's approach moves away from teleoperation or reinforcement learning on physical robots, potentially accelerating robot deployment across manufacturing and service industries.

Reward AI’s OM-1 Model: Why Human Motion Capture Is Accelerating Robot Training US-based Reward AI has released videos showcasing its Omnibody Model 1 OM-1 foundational AI model for robots, which learns complex tasks solely from human movements captured by a wearable device. Dick Weisinger · September 19, 2026 · 2 min read · Source: ITmedia NEWS · Issue 101 East Asian Technology Intelligence Japan & China tech news — translated, contextualized, and delivered for Western readers. Free. Unsubscribe anytime. This story ran in Issue 101, alongside three other stories. Robotics & Automation US-based Reward AI has released videos showcasing its Omnibody Model 1 OM-1 foundational AI model for robots, which learns complex tasks solely from human movements captured by a wearable device. The videos, presented at 1x speed, show robots adeptly performing tasks like packing smartphones, mixing cocktails, folding laundry, and unplugging Ethernet cables, drawing widespread attention on X for their speed and fluidity. OM-1 is designed for cross-robot compatibility, from industrial arms to humanoids, and can learn new tasks from less than 30 minutes of human demonstration. This development represents a step towards more intuitive and rapid robot programming, moving away from complex teleoperation or reinforcement learning on physical robots. For Western readers, it points to a potential acceleration in robot deployment across diverse industries, from manufacturing to service, by simplifying the teaching process.