Manipulation skills involving contact and friction are inherent to many robotics tasks. Using
the class of motor primitives for peg-in-hole like insertions, we study how robots can learn …

Reinforcement learning (RL) for continuous state/action space systems has remained a
challenge for nonlinear multivariate dynamical systems even at a simulation level …

Complicated assembly processes can be described as a sequence of two main activities:
grasping and insertion. While general grasping solutions are common in industry, insertion …

This paper synchronizes control theory with computer vision by formalizing object tracking
as a sequential decision-making process. A reinforcement learning (RL) agent successfully …

A key component of many robotics model-based planning and control algorithms is physics
predictions, that is, forecasting a sequence of states given an initial state and a sequence of …

The distribution line network is the electric power infrastructure directly facing the users, with
the characteristics of large coverage and complex network, and its operation safety is …

Peg-in-hole insertion is a critical process in industrial production. Traditional peg-in-hole
insertion methods are based on planning the robot's motion trajectory through the analysis …

Process industries involve processes that have complex, interdependent, and sometimes
uncontrollable/unobservable features that are subject to a variety of uncertainties such as …

In deep reinforcement learning (DRL), policy gradient (PG) and actor-critic (AC) based
methods are among the most populous and effective methods for training DRL agents. One …

This thesis presents planners and controllers for robust physics-based manipulation in real-
time. By physics-based manipulation, I refer to manipulation tasks where a physics model is …