Steering control for autonomous vehicles at high speeds is challenging due to the highly
nonlinear vehicle dynamics. The traditional model-based controllers usually degrade …

Deep neural networks (DNNs) used in computed vision have become widespread
techniques commonly used in autonomous embedded systems for applications such as …

In this paper, we study the cooperative consensus control problem of mixed-order (also
called hybrid-order) multi-agent mechanical systems (MMSs) under the condition of …

Recent years have witnessed rapid development of autonomous driving. Model-based and
model-free reinforcement learning are two popular learning methods for autonomous …

General contact-rich manipulation problems are long-standing challenges in robotics due to
the difficulty of understanding complicated contact physics. Deep reinforcement learning …

The autonomous driving aims at ensuring the vehicle to effectively sense the environment
and use proper strategies to navigate the vehicle without the interventions of humans …

In this paper, we continue our prior work on using imitation learning (IL) and model free
reinforcement learning (RL) to learn driving policies for autonomous driving in urban …

Explainability is essential for autonomous vehicles and other robotics systems interacting
with humans and other objects during operation. Humans need to understand and anticipate …

Policy learning for delicate industrial insertion tasks (eg, PC board assembly) is challenging.
This paper considers two major problems: how to learn a diversified policy (instead of just …

The dual-clutch transmission (DCT) was developed to increase the transmission efficiency
and the shift performance. However, in a DCT, due to uncertainty related to the actuator, the …