Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics

Understand

We present a novel approach to shared control of human-machine systems.

• Our method assumes no a priori knowledge of the system dynamics.
• Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator.
• Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input.