This article proposes a novel stochastic-skill-level-based shared control framework to assist
human novices to emulate human experts in complex dynamic control tasks. The proposed …

Predictive simulations of human motion are a precious resource for a deeper understanding
of the motor control policies encoded by the central nervous system. They also have …

Robots with a modular body permit a wide range of physical configurations, which can be
obtained by arranging the composing modules differently. While this freedom makes …

Human behavior modeling is important for the design and implementation of human-
automation interactive control systems. In this context, human behavior refers to a human's …

The choice of objective is critical for the performance of an optimal controller. When control
requirements vary during operation, eg due to changes in the environment with which the …

Inverse optimal control (IOC) is about estimating an unknown objective of interest given its
optimal control sequence. However, truly optimal demonstrations are often difficult to obtain …

Inverse optimal control (IOC) algorithms can be used to reveal underlying objectives.
Existing algorithms commonly estimate the objectives by assuming that the cost function can …

Robotic software design and implementation have traditionally relied on human engineers
to fine-tune parameters, optimize hardware utilization, and mitigate unprecedented …

Given a set of observations, the goal of inverse optimization is to impute parameter values of
an optimization model such that the observations become optimal (or, if this is not possible …

Despite many studies in human motion analysis using optimal control theory to understand
how movement is generated, less attention is focused on the structure of the optimal …