Remy AI captures real‑world human movement data to train embodied AI systems. By collecting in‑the‑wild motion recordings of people performing everyday tasks, it provides large, context‑rich datasets that let robots and virtual agents learn habits, intuitions, and nuanced behaviors directly from the masters of their craft.
Funding
Funding not disclosed
Founders
Product
Problem
Physical AI systems often rely on synthetic or lab‑collected data that lacks the subtle, context‑specific motions exhibited by skilled humans in real environments. This gap leads to models that struggle with nuanced, real‑world tasks and requires extensive manual tuning before deployment.
Solution
Remy AI generates large‑scale datasets by capturing human movement directly in natural settings. Using sensor‑fusion and computer‑vision pipelines, the platform records the full body kinematics, object interactions, and environmental context of practitioners as they perform their work. The collected data is annotated and packaged for training embodied AI models, enabling them to inherit human habits, intuitions, and adaptive behaviors. By providing authentic, in‑the‑wild motion data, Remy AI reduces the reliance on costly simulation and accelerates the transition of physical AI from prototype to real‑world operation.
Target Audience
Primary customers are robotics developers, autonomous system manufacturers, and research labs building embodied AI that require realistic human motion data for training and validation.
Features
- Multi‑modal capture system combining depth cameras, inertial measurement units, and environmental sensors for high‑fidelity motion tracking
- Automated annotation pipeline that labels actions, object contacts, and contextual cues without manual labeling
- Scalable data collection framework that supports diverse domains such as manufacturing, logistics, and service robotics
- Secure data hosting and API access for seamless integration into machine‑learning training pipelines
- Continuous dataset updates reflecting evolving human techniques and new task variations