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# Reward AI's OM-1 learns manipulation from gloved humans, not robot data
- URL: https://globalfeed.ai/en/reward-ais-om-1-learns-manipulation-from-gloved-humans-not-robot-data/
- Published: 2026-09-15T21:14:15.000Z
- Updated: 2026-09-15T21:14:15.000Z
- Description: Reward AI unveiled OM-1, a manipulation policy trained only on human demonstrations captured by a sensorized glove, with no teleoperation or on-robot data.
- Author: GlobalFeed Editor
- Tags: Reward AI, robotics, OM-1, x-marktechpost, dil-en, denetim-gerekli, video

Robotics startup Reward AI has released OM-1, short for Omnibody Model 1, a general-purpose manipulation policy that learns from people wearing a sensorized glove and then runs on industrial arms and humanoids at human speed. The claim the company highlights: no teleoperation data and no on-robot data enter training. The team's prior work includes DexCap, HumanPlus and ALOHA.

OM-1 is currently an in-house policy. No weights, code, dataset or API have been published, so developers cannot run it on their own hardware.

## The glove and the data interface

The stack starts with Omnibody Hand, a wearable that builds on the team's earlier DexCap portable motion capture work. Instead of copying the human hand joint by joint, it uses a seven-degree-of-freedom design centred on choosing contact points, reorienting objects in hand, and switching between precision and power grasps. The glove combines high-frequency tactile sensing, proximity sensing for the pre-contact approach, and global-shutter in-hand cameras. A distal flexion mechanism absorbs differences in finger length, removing per-user adjustment.

The only quantitative result disclosed concerns hand pose tracking. Moving trackers between two mechanical stops at eight speeds from 3 to 67 cm/s, averaged over ten runs each, electromagnetic tracking rose from about 0.4 mm to 9.5 mm of mean overshoot error, while visual-inertial tracking rose from about 2.1 mm to 24.9 mm. At the highest speed that is a 60% reduction, with a narrower run-to-run spread.

## Control layer

OM-1 generates robot actions directly from human motion. Because every demonstration arrives in the same format, there is no split between pre-training and post-training: a single stage trains a single policy. Each modality is processed at its sensor's native sampling rate. Beneath the policy sits a high-frequency control layer trained with reinforcement learning in simulation, handling external disturbances and system delays while motion continues during inference.

Reward AI says OM-1 picks up a new task from less than 30 minutes of human data. No success rates, public baseline comparisons or paper have been released, so the claims rest on demonstrations. [Details at MarkTechPost](https://www.marktechpost.com/2026/09/14/reward-ai-releases-om-1-a-robot-policy-trained-on-human-demonstrations-only-with-no-teleoperation-or-on-robot-data/?ref=globalfeed.ai).