Deep Learning

AI for Robotics

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.

Why MATLAB?

Synthetic Training Data Generation

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

Simulate Lidar Sensor and Generate Point Cloud Data
Depth and Semantic Segmentation Visualization for UAV Using Unreal Engine Simulation

Object Identification and Mapping

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

Object Detection with YOLOv4
Map and Classify Urban Environment Using UAV Camera and Deep Learning

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.

Train Deep Learning-Based Sampler for Motion Planning
Accelerate Motion Planning with Deep-Learning-Based Sampler

System Level Testing and Deployment

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

Pick-and-Place Workflow in Gazebo Using ROS
Sign Following Robot Using YOLOv2 Detection Algorithm with ROS in Simulink

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Deep Learning

Design and implement deep neural networks with algorithms and pretrained models.

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Machine Learning

Engineer features, train machine learning models, and tune hyperparameters.

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