Dexym AIBook DemoFrom video to robot motion, building the cutting-edge data pipeline for Embodied AI
SynaData breaks beyond the limits of lab-bound embodied AI data collection through a dual-engine approach: distributed real-world capture points and large-scale internet video conversion.
Compared with costly, low-throughput teleoperation and motion-capture pipelines, SynaData expands potential annual data supply to the billion-scale by leveraging passive capture in real operational environments and efficiently converting global video sources into training data.
We turn real-world interaction into learning signals for robots — at scale, at low cost, and with the long-tail coverage required for real deployment. This approach has already been validated through deliveries to leading humanoid robotics companies and research institutions.
Research & Open Source
HORA is an industry-first multimodal embodied AI training dataset extracted from real-world human video. Built to address the scarcity and cost of robot training data, HORA replaces traditional manual teleoperation collection with Dexym’s proprietary video-to-data pipeline, converting large volumes of human operation video into robot-learnable training data. The dataset contains more than 150,000 high-quality trajectories and introduces key innovations in both structure and modality.
RoboWheel is a robotics data engine built for cross-embodiment, multimodal, and high-fidelity physical interaction data. It provides a full pipeline from human operation video to robot-usable data, while opening a critical path toward scalable, physically grounded HOI data for robot foundation models.