Reward AI launches OM-1 to run one policy across robot bodies
The Stanford robotics spinout says its model learns from wearable human demonstrations, without teleoperation or robot training data.
By Ryan Merket · Published
Primary source: Reward AI on X
Why it matters
OM-1 targets the data reset that follows each new robot design. Reusing human demonstrations across hardware could cut deployment time and make manipulation data a durable asset.
Reward AI co-founders Zipeng Fu (@zipengfu) and Chen Wang (@chenwang_j) launched OM-1 on September 14th, pitching a robot foundation model that can control tabletop arms, industrial systems and humanoids without robot-specific fine-tuning.…
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.