ROBOTICS & AVs
Edge cases found before the field finds them.
Perception data, plus the operations layer that keeps it accurate long after deployment.
Autonomous systems face an infinite variety of edge cases in the physical world. LexData provides the continuous perception validation that keeps robotic navigation, manipulation, and decision-making reliable - even when environments change.
95%+ Navigation reliability in changing environments
The case study ↓// From this field · vehicle · open landing spot · person
Real footage · real labels · unretouched product output
// 01 · What breaks AI in robotics
Edge cases are infinite
Every new environment, lighting condition, object placement, and human interaction creates a potential failure mode. You can't train for every scenario - you need a system that adapts.
Perception quality degrades silently
Calibration drifts, lenses get dirty, lighting changes - your robot's perception slowly degrades, and nobody notices until it misses a pallet or misidentifies an obstacle.
Training data quality is a liability
Crowd-sourced annotation with inconsistent quality introduces noise into safety-critical perception systems. A mislabeled pedestrian in AV training data isn't a data quality issue - it's a safety risk.
// 02 · Inside LexLoop
LexAnnotate
Pixel-Perfect Data
Pixel-level semantic segmentation, LiDAR point cloud annotation, sensor fusion labeling, and temporal event tracking for complex perception tasks. Expert annotators, accountable for every label.
LexInsight
Spatial Intelligence
Build spatial understanding from flat labels. Map object relationships, track movement patterns, identify zero-day anomalies that standard training data misses.
LexAlert
Perception Alerts
Continuously validate perception performance in production. Detect when sensor drift or environmental changes degrade navigation. Route failures back for retraining before they cause incidents.
// 03 · Where it runs
Sensor Fusion Labeling
Synchronized annotation for camera, LiDAR, and radar data streams.
Navigation Edge Cases
Identifying and labeling corner cases for path planning algorithms.
Manipulation & Grasping
Precise 3D object pose estimation for robotic arms.
Warehouse Spatial Mapping
Dynamic updating of facility maps for AMR fleets.
Pedestrian & Obstacle Detection
Safety-critical object classification in crowded environments.
Dynamic Environment Tracking
Monitoring changes in physical spaces over time.
// 04 · In production
Dynamic Spatial Navigation
95%+
Navigation reliability in changing environments
Read the case study (PDF) ↓- The Challenge
- Flat bounding boxes failed to provide spatial context for autonomous material handling in shifting environments.
- Our Execution
- Deployed pixel-level semantic segmentation and temporal event tracking for high-fidelity spatial datasets.
- The Result
- Enabled autonomous systems to adapt to changing environments safely. Reduced manual overrides.
// 05 · Why LexData for robotics
01
Full-time domain experts who put their name on every label.
02
Continuous perception validation in production, not just training data delivery.
03
99%+ accuracy with human verification for safety-critical systems.
04
Proven with Bonsai Robotics for autonomous outdoor navigation.
// 06 · Same loop, different field
Bring us your robotics edge cases.
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