A human-like learning control for digital human models in a physics-based virtual environment

This paper presents a new learning control framework for digital human models in a physics-based virtual environment. The novelty of our controller is that it combines multi-objective control based on human properties (combined feedforward and feedback controller) with a learning technique based on human learning properties (human-being’s ability to learn novel task dynamics through the minimization of instability, error and effort). This controller performs multiple tasks simultaneously (balance, non-sliding contacts, manipulation) in real time and adapts feedforward force as well as impedance to counter environmental disturbances. It is very useful to deal with unstable manipulations, such as tool-use tasks, and to compensate for perturbations. An interesting property of our controller is that it is implemented in Cartesian space with joint stiffness, damping and torque learning in a multi-objective control framework. The relevance of the proposed control method to model human motor adaptation has been demonstrated by various simulations.