Planet-ScaleDatafortheReal World
We focus on real-world AI: robotics and world models. Today’s systems degrade under distribution shift, long-horizon tasks, and real-world uncertainty.
Modern AI systems reason well in text and images, but they struggle when deployed in the real world. Agents fail on long-horizon tasks. Robots behave brittly outside narrow distributions. Planning degrades when environments are novel, stochastic, or partially observed.
Scaling has not closed this gap. Larger models and longer contexts improve benchmarks, but deployed performance in physical environments remains fragile.
This is Not a Architecture Problem
It is a data problem.
The dominant training paradigm—static corpora, scraped text, short clips—does not provide the experience required to learn how the world evolves under action.
Models trained on static snapshots cannot learn causality, temporal dynamics, or the consequences of intervention. They see the world frozen, never in motion.
Interaction, Not Description
We are entering an era where models must learn from interaction, not description. The next generation of foundation models will be trained not on what the world looks like, but on how it behaves—action by action, state by state.
Arcterra is a real-world data research lab building the datasets, environments, and infrastructure required for this shift.
From Representations to Experience
“Language models learned from the written record of the world. Embodied agents must learn from the world itself.”
Solving real-world, long-horizon problems requires data that captures state evolution over time, action-to-consequence chains, multi-step intent and recovery, and distributional diversity across environments.
This kind of learning cannot be bootstrapped from text or images alone. It requires spatiotemporal, action-conditioned data at scale. Our work focuses on producing that data, end-to-end.
A Real-World Data Research Lab
Research lab first, not a data broker.
We develop theses on where real-world AI is headed—how agents, world models, and robotics systems are evolving—and use those theses to guide what data should exist but doesn’t, how it should be collected, how it should be structured and evaluated, and what benchmarks and environments are missing.
Data, evals, benchmarks, and environments are treated as a single system.
Our Products
Three vectors of development
Action-labeled, long-horizon gameplay
World Model Gaming Data
- FPV 1080p, 30–60 FPS
- Native action + control logs
- 5–120+ minute trajectories
- Diverse environment coverage
- Visual Understanding
- IP Rights
Built for world models and long-horizon agents.
Continuous first-person task execution
Robotics Egocentric Data
- 1080p head-mounted FPV
- Hands-in-frame manipulation
- Multi-step real-world tasks
- Action-aligned timestamps
Designed for embodied and world-aware robotics models.
Training-grade sensor infrastructure
Custom Capture Hardware Glasses
- Stable 1080p continuous capture
- Fixed, repeatable camera geometry
- Long-duration battery
- Synced logging + upload
Consistent data at scale for real-world AI.
Our Approach
Data as a first-class research artifact.
Collection, annotation, evaluation, and deployment are co-designed so that datasets are aligned with how models are actually trained, suitable for world-model and agent learning, reusable across tasks and domains, and structured for long-horizon learning rather than demos.
The goal is not volume alone, but useful experience.