Large-scale, high-quality data collection for imitation learning, reinforcement learning, evaluation, and physical AI foundation model workflows.
Planning regional data operations? Check the operations map to see where SVRC can support local collection and rollout.
We help robotics and AI teams collect large-scale, high-quality real-world interaction data for learning-based systems, with a focus on manipulation, contact-rich tasks, and human-robot interaction.
Our workflows are designed for teams building imitation learning models, reinforcement learning systems, and foundation models for physical AI, where data quality, consistency, and reproducibility matter more than raw volume.
Move from real episode to structured packet, benchmark run, failure replay, and back into training.
Vision, proprioception, tactile/contact signals, human actions, and environment context.
Task-ready datasets, repeatable collection procedures, capability scoping, and delivery packaging.
We do not just collect data. We close the loop: real episode to structured packet to benchmark run to failure replay to back to training. When robots fail, we extract failure packets such as keyframes, contact slices, and correction trajectories, then feed them into the next policy version. Failures become assets.
This is what makes us different from generic data vendors: we operate at the intersection of real hardware, learning-based control, and research-grade data standards. Our team understands both robotics systems and ML pipelines.
We specialize in multimodal, synchronized robotic datasets captured from real hardware in controlled and semi-structured environments.
All modalities are time-synchronized, structured, and validated before delivery.
For manipulation and skill learning tasks, we deploy human-in-the-loop teleoperation systems to capture demonstrations that reflect real human intent, correction behavior, and adaptation under contact.
We do not collect unstructured raw logs. Each project begins with explicit task and dataset design: task definition, success criteria, state/action/observation specs, episode segmentation, sensor coverage, and failure modes to include. The result is directly usable for training, evaluation, and benchmarking.