Data Collection
Large-scale, high-quality real-world data for learning-based robotic systems. Built for imitation learning, RL, and foundation models.
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.
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Our data collection 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.
The Data Loop — From Failure to Training
We don't just collect data. We close the loop: real episode → structured packet → benchmark run → failure replay → back to training. When robots fail, we extract failure packets (keyframes, contact slices, correction trajectories) and feed them into the next policy version. Failures become assets. The loop accelerates.
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.
What We Collect
We specialize in multimodal, synchronized robotic datasets captured from real hardware in controlled and semi-structured environments.
- Vision — RGB, RGB-D, multi-view camera streams, time-aligned with robot state and control
- Proprioception — Joint position, velocity, torque, motor currents, and low-level control signals
- Force & Tactile — End-effector force, distributed tactile arrays, contact location, pressure, and shear
- Human Inputs — Teleoperation commands, demonstration trajectories, corrective actions
- Environment Context — Scene configuration, object metadata, task parameters, episode boundaries
All modalities are time-synchronized, structured, and validated before delivery.
Human-in-the-Loop Teleoperation
For manipulation and skill learning tasks, we deploy human-in-the-loop teleoperation systems to capture high-quality demonstrations that reflect real human intent and adaptation.
- Anthropomorphic control mappings for intuitive demonstrations
- Real-time gravity compensation and compliance
- Safe operation during contact and failure cases
- Repeatable task initialization and reset procedures
Task-Driven Dataset Design
We do not collect "raw logs" without structure. 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 resulting dataset is directly usable for training, evaluation, and benchmarking.
Use Cases
Our data collection services are commonly used for:
- Manipulation policy training
- Tactile-aware grasping and insertion
- Sim-to-real transfer validation
- Human–robot interaction research
- Failure analysis and robustness testing
We work with teams across research labs, startups, and industry R&D groups.
Engagement Models
We support flexible engagement models: one-off projects, ongoing dataset expansion, pilot studies, and long-term partnerships. We work under NDA and adapt to internal data governance and security requirements.
Why Silicon Valley Robotics Center? Unlike generic data vendors, we operate at the intersection of real robotic hardware, learning-based control systems, and research-grade data standards. Our team understands both robotics systems and ML pipelines.