Field Motion
Field Motion operates a physical intelligence data platform that collects, curates, and delivers large-scale real-world human motion demonstration datasets—including egocentric, stereo, MONO, and UMI formats—specifically designed for humanoid robot training and physical AI development. The company emphasizes structured collection programs with coverage planning, quality gates, and training integration pilots to ensure datasets are usable for downstream learning pipelines.
- Robotics
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Training humanoid robots and physical AI systems requires massive amounts of real-world human motion demonstration data to achieve reliable, adaptable behavior. Without access to large-scale, high-quality physical demonstration datasets, robotics developers struggle to train models that generalize across diverse tasks and environments.
Solution
Field Motion operates a physical intelligence data platform that collects, curates, and delivers real-world human motion demonstration datasets specifically designed for humanoid robot training. The platform captures human movements and activities in real-world settings, transforming them into structured datasets that robotics teams can use to train their AI models. Field Motion supports multiple data modalities—egocentric video, stereo capture, monocular video, and UMI demonstrations—each serving a distinct role in robot learning. The company emphasizes designing coverage plans, defining capture specifications, applying quality gates, and running training integration pilots before committing to production volume, ensuring datasets are usable for downstream learning pipelines.
Target Audience
The primary customers are robotics companies, AI research labs, and developers building humanoid robots or physical AI systems that require extensive human motion demonstration data for model training.
Features
- Large-scale collection of real-world human motion demonstration data across egocentric, stereo, MONO, and UMI formats
- UMI (Universal Manipulation Interface) demonstrations with handheld grippers, relative-trajectory action representations, and gripper signal tracking for robot-compatible action supervision
- Coverage planning that specifies task families, object variations, environments, participants, and success criteria before recording begins
- Episode-level labeling with task instructions, start/end boundaries, outcomes, and handling of interruptions or failed attempts
- Quality gates checking hand visibility, dropped frames, synchronization, tracking failures, and duplicate episodes, with accepted data reported separately from raw footage
- Training integration pilot service that loads a small batch into the intended learner before committing to production volume
- Annotation and development services alongside data collection to support physical AI training pipelines