Digital information understanding
Internet-scale text, images, and video created the pre-training substrate for screen-native models.
Physical AI Data Infrastructure
Egocentric data infrastructure for Physical AI.
Zerolaw converts real-world human observation and action into structured ego data: task-ready episodes for world modeling, physical reinforcement learning, and action fine-tuning.
- 10k+
- hours of field data experience
- 200k+
- developer ecosystem experience
- 15+
- years in CV, SLAM, VIO, and SFM
The bottleneck
Screen AI has the internet. Physical AI needs the physical world.
Observation and action in the real world
Robots and embodied systems need multimodal, task-grounded data from physical environments.
Product architecture
From hardware systems to reusable data assets.
Zerolaw closes the loop between collection, processing, annotation, and corpus construction so model builders can train on consistent Physical AI episodes instead of raw footage.
ZL Capture
Multi-model capture systems for different collection needs, environments, and data quality targets.
ZL Core
Automated cleaning, slicing, synchronization, sensor calibration, and sensor fusion.
ZL Annotation
Human-in-the-loop labeling with feedback that calibrates platform rules and data schemas.
ZL Corpus
Reusable, standardized, trainable corpora and derived datasets for model development.
Physical AI Episodes
One continuous experience, synchronized into learnable signals.
- RGB and stereo video
- Head, hand, and object pose
- IMU and motion streams
- Depth, maps, and task labels
Hardware systems
Hardware controlled for data quality, dimensionality, and cost.
Explore ZL Capture hardware
ZL Capture V1
High-dimensional wearable hardware
Built for dense multimodal collection when higher sensor frequency and explicit depth are required.
- Fisheye grayscale VGA x2
- RGB HD x2
- IMU 1000Hz x2
- Depth x1
- 6DoF head pose x1
ZL Capture L1
Lightweight egocentric capture
Designed for portable human demonstration collection with core vision and inertial signals.
- RGB HD x2
- IMU 200Hz x1
- Synthetic depth x1
- 6DoF head pose x1
Model outcomes
Built to fit Physical AI training loops.
World modeling
Egocentric observations help models learn how environments, objects, and people evolve over time.
Physical RL
Task-grounded episodes provide structure for simulation, alignment, and reinforcement learning workflows.
Action fine-tuning
Human demonstrations become multimodal supervision for downstream embodied and robotic behaviors.
Build with Zerolaw
Define the data protocol for your Physical AI training pipeline.
Tell us what you are building and the kind of real-world data you need. We will follow up with the right hardware, annotation, or corpus path.
contact@zerolaw.ai