Capture, Contribute, Earn Capture, Contribute, Earn


Contributors capture first-person video of daily household activities using a phone or head-mounted device.
Authentic, diverse, and cluttered — just like the real world robots must navigate and organize.
Contributed by real people worldwide, capturing true spatial and environmental diversity.
Building the foundational data infrastructure for truly capable, context-aware embodied AI.
The Robotin Network ecosystem is built on a multi-tiered framework.
The Data Collection Layer forms the foundation of Robotin Network, utilizing household devices to capture high-fidelity, real-world data— ranging from perception images to ego-centric task video.
This decentralized approach ensures diverse, high-quality data from everyday environments, vital for the development of embodied AI.
After data is contributed, it is processed and refined in the Data Processing and Refinement Layer.
Here, the raw data is enhanced and structured by expert data professionals into valuable, actionable datasets ready for AI training and robotic system development.
The refined data is then made available in the Data Consumption and Application Layer, where it can be utilized by various industries such as physical AI and household robotics.
These applications rely on high-quality datasets to enhance their functionality and efficiency, driving the future of embodied intelligence.
Through the Tokenization and Value Exchange Layer, ROBOTIN tokenizes the real-world data.
This allows data contributors to earn tokens for their contributions, while data consumers can access and utilize these datasets to further their work in AI and robotics development.
Training embodied AI systems requires diverse, high-quality data across multiple modalities. Robot manipulation datasets — captured via wrist-mounted cameras on robot arms — record end-to-end action sequences and are widely used in research and industry training pipelines. Ego-centric video is another key data type in this space: first-person footage of real people performing everyday tasks in real homes. Robotin is building a decentralized network to collect this data at scale, sourced directly from contributors around the world.
Scattered objects, tangled cables, dense clutter — contributed from everyday homes fuels perception models that help robots understand and organize real-world environments.
Embodied AI learns by interacting with the real world. Training requires large-scale data, enabling robots to perceive, understand, and act with human-like adaptability. The market is huge.
ROBOTIN is a decentralized, global platform for Physical AI and Embodied Intelligence. Training data collection, processing, and storage.
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