Solvitude builds high-quality egocentric, multimodal and interaction-rich datasets for Physical AI, robotics and Vision-Language-Action models. We capture how people see, move, manipulate and interact with the physical world — then transform those interactions into structured training data for intelligent machines.
Our collection network enables scalable first-person data collection across homes, kitchens, factories, warehouses, workshops, hospitality and industrial environments. From everyday activities to complex operational workflows, we capture the physical interactions robots need to learn.
Multimodal by Design
Every Object Has a Different Physical Requirement. Our datasets can represent interactions with fragile, deformable, rigid, heavy and high-resistance objects, helping models understand how movement and interaction change according to the object and task.
Observe. Understand. Act.
We transform human demonstrations into structured action sequences that connect visual context, language, movement and outcome. This supports VLA training, imitation learning and robotic manipulation.
More People. More Environments. More Reality. Our distributed collection network allows the same task to be captured across different people, environments, objects and conditions — creating diverse datasets for models designed to operate beyond controlled environments.
Precision Is the Product. Our quality pipeline combines collection validation, annotation review and multi-stage QC to maintain temporal consistency, annotation accuracy and interaction-level precision. Every dataset is structured around the requirements of the model it is built to train.
