Implementation of singularity-free inverse kinematics for humanoid robotic arm using Bayesian optimized deep neural network

Abstract

This study presents the efficacy of deep learning techniques in controlling the arm of a humanoid robot without resorting to inverse kinematic analysis. Emphasizing real-time applicability, a straightforward deep neural network (DNN) structure is developed and optimized using hyperparameter Bayesian optimization. The Keras Bayesian Optimization Tuner with Gaussian process fine-tunes the DNN architecture. Utilizing a simulation environment and generating around 10 billion datasets, the optimized DNN is evaluated on both simulated and real robots. Impressively, the trained DNN exhibits a notable ability to predict robot control within a 25 ms timeframe, achieving a total Mean Absolute Error (MAE) of 0.02 for the xyz-axes. These results underscore the potential of deep learning-based approaches in humanoid robot arm control, eliminating the need for inverse kinematic equations and providing valuable insights for future robotic system development.