WITH SUSHIL KANNAN SENGUTTUVAN BALAMURUGAN AND DR. NIANYI LI, DEPARTMENT OF COMPUTING
Physics-based Deep Learning for Computer Vision
Physics-based deep learning combines principles of physics with machine learning to enhance the functionality of computer vision systems. This approach focuses on understanding the motion field, which is the projection of the velocity of 3D surface points onto a visual sensor's imaging plane. Accurate computation of the motion field is essential for various vision-based technologies, including video compression, image interpolation, 3D reconstruction, and robotics navigation. In the robotics field, physics-based deep learning is crucial for enabling robots to perceive, analyze, and interact with their environment. By integrating deep learning models like ResNet for image recognition and object detection, these systems can control robotic arms to perform specific tasks, such as picking up and placing objects. This integration allows for precise manipulation and enhances the robot's ability to operate in real-world scenarios.
This project utilizes the Interbotix X-Series Arms from Trossen Robotics to demonstrate the application of physics-based computer vision, showcasing the synergy between deep learning, computer vision, and real-world physics in robotics.
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