Reward AI
Reward AI provides the Omnibody stack, a unified robotics intelligence pipeline that captures human manipulation data through a wearable seven-degree-of-freedom hand device and trains a single policy deployable across robot arms, humanoids, and mobile manipulators. The system operates at full human speed, with electromagnetic tracking that reduces overshoot error by 60% compared to visual-only methods.
- Robotics
- Artificial Intelligence
- Industrial Automation
- Manufacturing / Industry 4.0
- Hardware
Funding
Founders
Founder details are not available yet.
Product
Problem
Robot policies today are typically designed for a single embodiment or family of robots, requiring retraining and new data collection when hardware changes. Existing demonstration collection systems either constrain natural human movement or lose fidelity at human working speeds, limiting robot learning to slow, scripted tasks.
Solution
Reward AI's Omnibody stack unifies the entire robotics intelligence pipeline around one principle: one model, one data interface, any body. The Omnibody Hand is a wearable seven-degree-of-freedom capture device that records natural manipulation behavior without constraining the wearer to any robot's kinematics. Data flows through a common interface into a single model that operates across industrial arms, legged humanoids, and mobile manipulators. The system treats speed as a first-class problem, designing perception, prediction, and high-frequency control together so robots operate at human efficiency without pausing between actions. Demonstrations collected today retain value for future robot designs, creating data that appreciates rather than depreciates.
Target Audience
The primary customers are robotics manufacturers and industrial users deploying robot arms, humanoids, and mobile manipulators in warehouses, factories, and service environments such as kitchens.
Features
- Omnibody Hand wearable with seven degrees of freedom designed for functional dexterity including thumb-index pinching and power grasps
- Electromagnetic motion tracking augmented with visual-inertial sensing that reduces overshoot error by 60% at high speed compared to visual-only tracking
- High-frequency tactile feedback, proximity sensing, and global-shutter in-hand cameras achieving continuous data coverage during rapid manipulation
- One common data interface spanning all robot bodies, eliminating embodiment gaps and fine-tuning stages between data collection and deployment
- Unified model supports tabletop manipulation, whole-body work (bracing, leaning, stepping), and quadmanual collaboration scenarios
- Demonstrations collected today retain value for future robot designs, creating data that appreciates rather than depreciates