One Robot
ActiveWorld models for robot evals and training.
About
One Robot builds simulation environments that are realistic to see and realistic to interact with, so robotics teams can train and evaluate robot policies without being bottlenecked by robot time. Today, improving a VLA often means more real-world hours: setting up the scene, running trials, resetting, and repeating. This loop is slow, expensive, and hard to scale. For example, material handling and manufacturing assembly tasks, models need far more training and evaluation data than teams can collect in the real world. We use task-specific data to build world model-based simulation environments for hard manipulation tasks (for example, textiles and box folding). These environments help teams run more training and evals, find failure modes faster, and accelerate iteration on action policies with less dependence on real-world data collection and robot availability.
Founders · 2
Bringing robots to life using world models and machine learning. Previously built robot learning and perception systems at Industrial Next, Symbio Robotics, NASA JPL, and Google. Bootstrapped geospatial AI company, Crescer AI, to profitability.
Robotics, SW/FW. Previously built robot learning and control system at Industrial Next and helped build Dojo at Tesla.
Launch
Building world model-based environments for robot training and evaluation
One Robot builds world-model–based simulations that learn contact dynamics and visual appearance from robot data to create photorealistic, physics-faithful environments for training and evaluating robot action models, enabling more iterations with less real-world robot time. Target users are robotics teams training manipulators for tasks like textiles and box folding.
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