Data Collection for Learning-Based Robotics

We help robotics and AI teams collect large-scale, high-quality real-world interaction data for learning-based systems. 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.

What We Collect

We specialize in multimodal, synchronized robotic datasets: vision (RGB, RGB-D, multi-view), proprioception (joint state, torque, control signals), force & tactile (end-effector force, distributed tactile arrays), human inputs (teleoperation commands, corrective actions), and environment context (scene configuration, task parameters, episode boundaries). All modalities are time-synchronized, structured, and validated before delivery.

Task-Driven Dataset Design

We do not collect "raw logs" without structure. Each project begins with explicit task and dataset design: task definition and success criteria, state/action/observation specifications, episode segmentation and termination conditions, required sensor coverage and sampling rates, and failure modes to intentionally include. This ensures the resulting dataset is directly usable for training, evaluation, and benchmarking.

Human-in-the-Loop Teleoperation

For manipulation and skill learning, we deploy human-in-the-loop teleoperation systems. Our workflows support anthropomorphic control mappings, real-time gravity compensation and compliance, safe operation during contact and failure cases, and repeatable task initialization. This approach is particularly effective for imitation learning, dataset bootstrapping, and capturing recovery behaviors.

Dataset Structure & Delivery

Collected data is organized into episode-based datasets with per-episode metadata, time-indexed multimodal observations, control commands and robot state, and optional annotations. We support delivery in learning-ready tensor formats, ROS/robotics-native formats, and custom schemas aligned with client training pipelines.

Why Silicon Valley Robotics Center

Unlike generic data vendors or annotation platforms, 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 machine learning pipelines.