Recent development in the Wire arc additive manufacturing (WAAM) provides a promising
alternative for fabricating high value-added medium to large metal components for many …

This article reviews the state-of-the-art modeling and control techniques for aerial robots
such as quadrotor systems and presents several future research directions in this area. The …

Deep networks trained on demonstrations of human driving have learned to follow roads
and avoid obstacles. However, driving policies trained via imitation learning cannot be …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

Autonomous learning has been a promising direction in control and robotics for more than a
decade since data-driven learning allows to reduce the amount of engineering knowledge …

Garment folding is a ubiquitous domestic task that is difficult to automate due to the highly
deformable nature of fabrics. In this article, we propose a novel method of learning from …

Learning a policy using only observational data is challenging because the distribution of
states it induces at execution time may differ from the distribution observed during training …

In the field of robotic manipulation, the proficiency of deformable object manipulation lags
behind human capabilities due to the inherent characteristics of deformable objects. These …

This paper investigates how to weight imperfect expert demonstrations for generative
adversarial imitation learning (GAIL). The agent is expected to perform behaviors …

Learning policies that generalize across multiple tasks is an important and challenging
research topic in reinforcement learning and robotics. Training individual policies for every …