To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …

Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

The combination of deep neural network models and reinforcement learning algorithms can
make it possible to learn policies for robotic behaviors that directly read in raw sensory …

In this paper, we propose a real-world benchmark for studying robotic learning in the context
of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by …

We investigate the visual cross-embodiment imitation setting, in which agents learn policies
from videos of other agents (such as humans) demonstrating the same task, but with stark …

The application of robots in mechanical assembly increases the efficiency of industrial
production. With the requirements of flexible manufacturing, it has become a research …

In recent years, significant progress has been made in the field of robotic reinforcement
learning (RL), enabling methods that handle complex image observations, train in the real …

Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but
current RL methods require a large number of trials to accomplish this. In this paper, we …