The data infrastructure for
robot foundation models.
SignIQ Lab operates the capture, annotation, QA, and delivery infrastructure behind robot foundation-model training data. Instrumented facilities, expert operators, calibrated multi-modal sensors, and validated export pipelines — all under one workflow.
Our Mission
Build the data infrastructure for robot foundation models.
Modern robot policies — GR00T, π0/π0.5, OpenVLA, ACT, Diffusion Policy, world models, humanoid whole-body controllers — train on heterogeneous data: real teleop, autonomous rollouts, simulation, egocentric human video, retargeted motion, force/tactile, and language annotations. Sourcing all of that under one schema is the bottleneck.
We solve it with a vertically integrated stack: instrumented capture facilities, expert teleoperation crews, automated annotation and QA pipelines, and standardized export to LeRobot, GR00T-LeRobot, HDF5, and MCAP. Researchers scope what they need; we handle scenario design through delivery.
Teleoperation
Teleoperation Technology
VR Interfaces
Immersive, low-latency control for intuitive manipulation tasks.
Isomorphic Arms
Primary modality for high-precision, kinematic-accurate data.
Motion Capture
Full-body human tracking for retargeting and behavior cloning.
Sensors
Multi-Modal Sensor Suite
RGB Cameras
Head-mounted and wrist-mounted high-resolution RGB cameras.
Depth Sensors
High-fidelity depth maps for 3D spatial understanding and point clouds.
Tactile Feedback
Advanced hand-mounted sensors for force and texture perception.
Robotics
Supported Platforms
Formats
Supported Data Formats
LeRobot
RecommendedNative format for Hugging Face LeRobot framework. Optimized for immediate training.
HDF5
PopularStandard hierarchical format for high-performance storage of complex multi-modal data.
MCAP
FlexibleModern container format for multi-channel time-series data from robotics systems.
Environments
Collection Environments
Home Environment
Train robots for household assistance with realistic living rooms, kitchens, and bedrooms. Capture dual-arm manipulation for cooking, cleaning, object organization, and furniture interaction. Perfect for developing home helper robots that can autonomously cook meals, tidy spaces, and assist with daily chores.
Retail & Supermarket
Enable retail automation with fully equipped checkout counters, shelving systems, and product displays. Collect data for shelf restocking, inventory scanning, customer assistance, and product handling. Ideal for training robots to work alongside humans in grocery stores and retail environments.
Pharmacy
Develop precise medication handling with authentic pharmacy setups including medicine shelving, prescription counters, and automated dispensing systems. Train robots for error-free drug dispensing, inventory management, and pharmaceutical logistics with 99.9% accuracy requirements.
Logistics & Sorting
Master warehouse operations with industrial-scale logistics setups for bin picking, parcel sorting, and material handling. Capture high-speed sorting data (300+ items/min) and train AMRs for autonomous navigation in complex warehouse environments with dynamic obstacles.
Power & Utilities
Enable autonomous infrastructure inspection with electrical panels, substations, and utility equipment. Train quadruped and mobile robots for thermal monitoring, equipment health checks, and safety inspections in unmanned facilities, reducing downtime and improving safety.
Laboratory
Accelerate scientific research with automated lab bench tasks, pipetting, sample handling, and equipment operation. Train cobots and mobile manipulators to work alongside scientists, improving reproducibility and freeing researchers for high-value work.
Inspection
Develop autonomous monitoring systems with analog gauges, pressure meters, flow sensors, and industrial equipment. Train robots for continuous 24/7 inspection, gauge reading with computer vision, and predictive maintenance to replace episodic manual surveys.
Real-World Scenarios
Beyond our core scenarios, we continuously expand with 193 real-world environments spanning restaurants, hospitals, schools, apartments, and more — diverse settings including edge cases like extreme lighting, cluttered spaces, and dynamic human interaction.