Real-time inverse kinematics and inverse dynamics from motion capture

Abstract

This work applies machine learning to solving inverse dynamics and inverse kinematics tasks from the motion capture data. This approach may simplify the calculation process and help do scientific simulations as part of a physics engine that describes the neural control of human motion and decodes movement intent in individuals with neural damage. The existing algorithm has to be modified for every experiment and takes a significant amount of time to execute. It is also sensitive to noise and missing data, and it is not a real-time calculation. We propose a solution of inverse kinematics tasks with neural networks. Here we report accuracy results both on clean data and noisy data. We also apply a similar approach for the inverse dynamics task. The approach shows high accuracy on clean data, but this accuracy decreases if applied to the noisy data.