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 …

To enable safe and efficient human-robot collaboration in shared workspaces, it is important
for the robot to predict how a human will move when performing a task. While predicting …

To enable safe and efficient human-robot collaboration in shared workspaces, it is important
for the robot to predict how a human will move when performing a task. While predicting …

Researchers in biomechanics, neuroscience, human–machine interaction and other fields
are interested in inferring human intentions and objectives from observed actions. The …

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 …

The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take
actions in order to minimize the expected value of a cost function, ie, that humans are risk …

This article develops a methodology that enables learning an objective function of an
optimal control system from incomplete trajectory observations. The objective function is …

Path tracking controller of Autonomous Vehicles (AVs) plays an important role in improving
the dynamic behaviour of the vehicle. Model Predictive Control (MPC) is one the most …

Path Tracking Controller (PTC) plays a key role in achieving improved dynamic behaviour of
Autonomous Vehicle (AV) while it is mainly responsible for implementing the planned paths …

In this paper we present an inverse optimal control based transfer of motions from human
experiments to humanoid robots and apply it to walking in constrained environments. To this …