In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of
concern. The privacy problem is even more severe in autonomous driving business than the …

Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate
adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the …

The neural network policies are widely explored in the autonomous driving field, thanks to
their capability of handling complicated driving tasks. However, the practical deployment of …

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 …

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

Deep reinforcement learning has shown its effectiveness in various applications, providing a
promising direction for solving tasks with high complexity. However, naively applying …

Data-driven methods have great advantages in modeling complicated human behavioral
dynamics and dealing with many human-robot interaction applications. However, collecting …

The current learning pipelines for robotics manipulation infer movement primitives
sequentially along the temporal-evolving axis, which can result in an accumulation of …

The past decade has witnessed significant breakthroughs in autonomous driving
technologies. We are heading toward an intelligent and efficient transportation system …

Reinforcement learning methods have been developed to achieve great success in training
control policies in various automation tasks. However, a main challenge of the wider …