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, estimate 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.
Discover how to build AI models to enable autonomy
See How Customers Apply AI with MATLAB and Simulink
Learn about the products used with AI for robotics applications.