Custom Sequential Neural Network for Inverse Kinematic Regression

Inverse Kinematics (IK) is a fundamental problem in robotics, requiring the calculation of joint angles to achieve a desired end-effector pose. Traditional approaches to IK often rely on analytical or numerical techniques, which can be computationally expensive or prone to local minima. This paper presents a novel solution leveraging a custom Sequential Neural Network architecture specifically designed for inverse kinematic regression tasks. The proposed model incorporates a series of neural layers optimized for handling complex kinematic relationships and efficiently mapping inputs to outputs. The network is trained on synthetic datasets generated using a kinematic simulator and validated on real-world data from a robotic arm. By adopting a lightweight architecture with minimal computational requirements, the model enables rapid evaluation and training, making it suitable for resource-constrained environments. Although it may not always outperform deeper neural architectures in terms of accuracy, the proposed solution strikes a balance between computational efficiency and high precision. Furthermore, it effectively manages complex end-effector trajectories, providing robust performance for real-world applications. This work contributes to advancing efficient and practical IK solutions for robotics.