Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases
such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles …

Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in
control systems, robotics, Machine Learning (ML) and optimization taking into account the …

Inverse optimal control (IOC) assumes that demonstrations are the solution to an optimal
control problem with unknown underlying costs, and extracts parameters of these underlying …

To enhance the machines' intelligence, it is important for them to learn how humans perform
tasks. In this article, the issue of online adaptive learning human behavior is addressed for a …

There have been broad ranges of human-in-the-loop (HiTL) control systems, like fly-by-wire
aircraft, share driving, and energy management. To develop advanced HiTL control systems …

Inverse optimal control is the problem of computing a cost function with respect to which
observed state and input trajectories are optimal. We present a new method of inverse …

In this paper, we consider a dynamical system whose trajectory is a result of minimizing a
multiphase cost function. The multiphase cost function is assumed to be a weighted sum of …

This brief presents an inverse optimal control methodology and its application to training a
predictive model of human motor control from a manipulation task. It introduces a convex …

In this paper, we consider the problem of computing parameters of an objective function for a
discrete-time optimal control problem from state and control trajectories with active control …

One goal of artificial intelligence (AI) research is to teach machines how to learn from
humans, such that they can perform a certain task in a natural human-like way. In this article …