iKnet Model Improvements for Low Computing Resource Environments

Abstract

As the demand for inverse kinematics (IK) applications in embedded systems grows, developing efficient and accurate IK neural network models for resource-constrained devices remains a critical challenge. In this study, we propose two improved versions of the original IKNet: Improved IKNet and Focused IKNet. These models aim to enhance computational efficiency while maintaining accuracy. We evaluate the three IK models — Original IKNet, Improved IKNet, and Focused IKNet — under different computing environments, including both CPU and GPU settings. The evaluation focuses on three key metrics: memory usage, inference time, and accuracy. Experiment results show that both proposed models significantly outperform the baseline in inference speed and memory efficiency, especially in CPU-only environments, making them well-suited for deployment on embedded systems.

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