ARIA

Developed an end-to-end autonomous waste segregation system combining a 6-DOF robotic manipulator with a real-time computer vision pipeline for multi-class waste classification. Trained a custom YOLO-based object detection model to identify and classify waste categories (plastic, metal, organic, paper) with >90% accuracy under varied lighting conditions. Implemented inverse kinematics for precise pick-and-place operations, enabling the robotic arm to grip irregularly shaped objects and deposit them into the correct segregation bin.