Advancing Next-Generation Robotics

The robotics workforce of tomorrow depends on generative AI to navigate, interpret, and adapt to complex environments. From autonomous warehouse and industrial robots to next-generation service robots, these systems require high-quality, customized data to achieve accuracy and reliability. Coral Mountain Data delivers tailored data labeling solutions to fine-tune robotics models and support a seamless transition from simulation to reality (Sim2Real).

Our Robotics AI Data Solutions

With more than five years of expertise in AI training data and human-in-the-loop services, we understand the complexities of developing and deploying advanced robotics systems that learn and generalize across diverse tasks and domains. Here’s how Coral Mountain Data can support your mission:

Customized Data Annotation for Robotics Applications

Your robots are built for specialized tasks, and generic, off-the-shelf datasets simply won’t meet their needs. At Coral Mountain Data, we deliver custom data solutions and model evaluation services that empower robots to master complex, previously manual-only tasks—such as precisely picking and placing unfamiliar objects—with efficiency and accuracy.

Object Detection and Recognition

We deliver precise annotations for identifying tools, products, and obstacles—including machinery, equipment, and safety barriers—within industrial environments. This level of accuracy enables robots to interact with objects correctly, execute tasks with precision, and enhance workplace safety while boosting operational efficiency.

Scene Annotation

We provide high-precision labeling of environments with intricate details to enable seamless robot navigation. By capturing real-world complexities—such as uneven terrain, obstacles, and dynamic surroundings—our annotations empower robots with stronger spatial awareness, reducing navigation errors and improving adaptability in unpredictable settings.

Simulation Data Annotation

Splines are used in polyline annotations to classify and characterize objects. The use of polyline annotation techniques could lead to building AI for detecting diseases, analyzing irrigation patterns, and predicting harvest times on large farms.

LiDAR and Point Cloud Data Labeling

The use of GIS & Geospatial annotation enables AI-integrated farm drones to monitor plant health & farm condition, track farming equipment, and increase the efficiency of agricultural production.

Teleoperation

A Smarter Approach to Data Annotation for Incorporating AI in Agriculture

Annotating and labeling agriculture AI data to develop ML models for crop health and soil monitoring, livestock management, fructification detection, unwanted weeds detection, and other applications.