As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Imitation learning (IL) aims to extract knowledge from human experts' demonstrations or
artificially created agents to replicate their behaviors. It promotes interdisciplinary …

Imitation learning has been studied widely as a convenient way to transfer human skills to
robots. This learning approach is aimed at extracting relevant motion patterns from human …

Existing motion planning methods often have two drawbacks:(1) goal configurations need to
be specified by a user, and (2) only a single solution is generated under a given condition. In …

Automation of surgical tasks is expected to improve the quality of surgery. In this paper, we
address two issues that must be resolved for automation of robotic surgery: online trajectory …

In this paper, we present Combined Learning from demonstration And Motion Planning
(CLAMP) as an efficient approach to skill learning and generalizable skill reproduction …

Placing robotsoutside controlled conditions requires versatile movement representations
that allow robots to learn new tasks and adapt them to environmental changes. The …

This letter presents a novel algorithm for the continuous control of dynamical systems that
combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single …

The objective function used in trajectory optimization is often non-convex and can have an
infinite set of local optima. In such cases, there are diverse solutions to perform a given task …

Imitation learning is an important direction in the area of robot skill learning. It provides a
user-friendly and straightforward solution to transfer human demonstrations to robots. In this …