Apply AI to enable autonomy in robotics applications

With MATLAB, you can develop robotics applications with deep learning and reinforcement learning. You can enable autonomy for systems such as cobots, autonomous mobile robots, and UAVs with learning-based AI techniques. These techniques improve accuracy for robot perception and require less human intervention in decision-making.



Synthetic Training Data Generation

Build datasets by capturing and labeling images obtained from simulated and real-world scenarios.

Object Identification and Mapping

Use image recognition and object detection techniques to build maps, estimated robot poses, and detect dynamic obstacles.



Motion Planning and Controls

Speed up the sampling process for path planning by training a deep learning-based sampler. Use reinforcement learning for robot control.

System Level Testing and Deployment

Integrate AI models within Model-Based Design workflows. Build system-wise simulations and test with the AI models.

Learn More

Learn how to prepare training data

Preparing AI training data for robotics in MATLAB involves defining tasks, collecting diverse sensor data, preprocessing, labeling, splitting, and extracting features. With MATLAB’s Deep Learning Toolbox, create and refine models through training, validation, and testing, ensuring iterative improvement for robust performance.

Discover how to build AI models to enable autonomy

Explore the process of building AI models to facilitate autonomy, empowering systems to operate independently. This journey involves understanding the specific application, collecting relevant data, designing and training models, and implementing them to enable autonomous decision-making. Embrace the advancements in artificial intelligence to unlock the potential for autonomy across various domains and industries.


  • Lidar Toolbox
  • Deep Learning Toolbox

Download a FREE Trial           Request Consultation