IAES International Journal of Robotics and Automation (IJRA) Vol. 14, No. 1, March 2025, pp. 11~18 ISSN: 2722-2586, DOI: 10.11591/ijra.v14i1.pp11-18  11 Journal homepage: http://ijra.iaescore.com Design and development of humanoid robotic arm Shripad Bhatlawande 1 , Sakshi Kulkarni 1 , Shajjad Shaikh 1 , Sachi Kurian 2 , Swati Shilaskar 1 1 Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, India 2 Department of Biomedical Engineering, Rutgers University School of Engineering, New Brunswick, United States Article Info ABSTRACT Article history: Received Apr 6, 2024 Revised Oct 22, 2024 Accepted Nov 19, 2024 This paper presents the design, development, and evaluation of a 5-degrees of freedom (5-DoF) humanoid robotic arm featuring a sophisticated 5-finger gripper. The five degrees of freedom include the base, shoulder, elbow, wrist, and gripper, all controlled by MG996R servo motors to enhance grasping, positioning, flexibility, and mobility. The arm is constructed from laser-cut aluminum sheets. It effectively picks and places objects such as bottles and bags. A high-speed portable computing system is used to control robotics hand operations. A webcam is used for object detection and to acquire information about the surroundings. The system uses a convolutional neural network-based MobileNet architecture for object detection. The robotic hand is used as an assistive aid for amputees. It mimics finger movements based on detected objects. The system achieved a precision of 0.97 for bags and 0.93 for bottles, with accuracies of 96.83% and 92.42%, respectively. The system employs advanced computer vision algorithms and real-time strategies, ensuring adaptability across various tasks. It integrates advanced visual systems and improved feedback to enhance user interaction and overall usability. It addresses trade-offs between detection precision and processing time. Keywords: Assistive aid Deep learning End effectors Hand amputee Humanoid robotic arm This is an open access article under the CC BY-SA license. Corresponding Author: Swati Shilaskar Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology Pune, India Email: swati.shilaskar@vit.edu 1. INTRODUCTION This paper presents the design, development, and evaluation of a 5-degrees of freedom (5-DoF) humanoid robotic arm with a sophisticated 5-finger gripper. The 5 DoFs are the base, shoulder, elbow, wrist, and gripper. These resources are controlled with servo motors, enhancing the arm’s capabilities in grasping, positioning, flexibility, and mobility. Serving as the central control unit is the Raspberry Pi, directing the movements of the five servo motors through advanced algorithms to ensure seamless and precise arm operations. This robotic arm is developed for the rehabilitation of hand amputees and can be used for other industrial applications with certain modifications. Factors affecting limb movement and grasp are analyzed by measuring joint angles using Kinect depth sensors and MediaPipe Framework [1]. This emphasized the integrated approaches for real-time challenges. To oversee the system [2], handheld input devices like joysticks, keyboards, computer mice, and touch screens are commonly employed. The constraint of limited DoF presents a hurdle, particularly when managing robots with numerous degrees of freedom, such as robotic arms. Additionally, joystick manipulation of a robotic arm necessitates non-intuitive transitions between position, orientation, and gripping control modes. Nodes [3] can control the arm, plan safe movements, and execute actions to reach initial and final positions. Neural network-based [4] learning offers accurate continuous mapping and handles