Shihan Fu¹ and Ang Li², ¹USA, ²California State Polytechnic University, USA

Addressing the global challenge of inef icient waste management, my paper introduces an innovative recyclingsolution integrating machine learning, computer vision, and a robotic arm [1]. The background problemrevolvesaround inaccurate waste sorting and the environmental impact of recyclables ending up in landfills. The proposedsolution involves a sophisticated machine learning model for object recognition, a computer vision systemfor realtime detection, and a robotic arm for precise object manipulation [2]. Challenges included optimizing the machinelearning model for diverse materials and enhancing the robotic arm's adaptability. Experimentation involved testingthe system's ef iciency in various scenarios, showcasing its ability to recognize and sort recyclables accurately. Theresults demonstrated promising accuracy and adaptability. Ultimately, this solution of ers a practical andautomated approach to waste sorting, reducing environmental impact, and promoting ef icient recycling practices, making it a valuable tool for waste management systems globally [3].

Machine Learning, Robotics, Torch, Computer Vision