Labl'r.ai
Labl'r.ai provides purpose-built annotation pipelines for robotics manipulation data, handling high-frequency sensor streams like depth, RGB, and point clouds without frame drops. Its physics-grounded automated labeling produces annotations that transfer directly to hardware without domain adaptation, eliminating the need for manual labeling or domain adaptation work. The platform supports temporally segmented, frame-by-frame, and failure annotations, with enterprise deployments configured around each customer's data volume and pipeline requirements.
- Robotics
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Robotics companies developing manipulation systems face a critical shortage of high-quality annotated training data. Standard computer vision labeling tools are not designed to handle high-frequency sensor streams or to produce annotations that reflect real-world physics, forcing teams to spend significant time on manual labeling or on domain adaptation to make labels usable on actual hardware.
Solution
Labl'r.ai provides purpose-built annotation pipelines specifically for robotics manipulation data. The platform handles high-frequency sensor streams — including depth, RGB, and point clouds — without frame drops, ensuring that no critical data is lost during annotation. Its automated labeling is grounded in real-world physics, producing annotations that transfer directly to hardware without requiring domain adaptation. The system supports temporally segmented annotations, frame-by-frame segmentation, and failure annotations, giving robotics teams the full spectrum of labels needed to train robust manipulation models. Deployments are configured around each customer's data volume and pipeline requirements, with custom annotation SLAs and private cloud deployment options available for enterprise teams.
Target Audience
Robotics companies and research teams developing manipulation systems that need large-scale, physically accurate annotated datasets for training foundation models or production deployments.
Features
- High-throughput rendering pipeline optimized for robotics data streams including depth, RGB, and point clouds without frame drops
- Physics-grounded automated labeling designed so annotations transfer directly to hardware without domain adaptation
- Temporally segmented annotations for capturing dynamic manipulation sequences
- Frame-by-frame segmented annotations for precise object and action boundaries
- Failure annotations to train models on edge cases and failure recovery scenarios
- Custom annotation SLAs, tailored labeling pipelines, dedicated support engineer, and private cloud deployment options for enterprise customers