Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data
Reward AI, a robotics startup whose team’s prior work includes DexCap, HumanPlus, and ALOHA, has released OM-1, short for Omnibody Model 1. OM-1 is a general-purpose manipulation policy that learns from humans wearing a sensorized glove, then runs on industrial arms and humanoids at human speed. The key findings that stands out: no teleoperation data and no on-robot data go into training. The system follows one principle, ‘One Model, One Data Interface, Any Body,’. Is it de
A robotics startup has released a general-purpose robot policy trained entirely on human demonstrations captured through a wearable glove, without using teleoperation data or data collected directly from robots. The approach uses an integrated system where human motion capture, learning, and robot control work together as one pipeline, allowing the same demonstrations to potentially train different robot bodies. Most robot policies train on robot-specific data, but this system argues that human-level manipulation requires a fundamentally different approach to how data is captured and used. The policy learns new tasks from less than thirty minutes of human demonstration data and can run on industrial arms, humanoids, and mobile manipulators, though the system is not yet publicly available.
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Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation
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Memories.ai builds standardized egocentric video pipeline
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