The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or
otherwise inaccessible environments. Although most legged robotics research to date …

Research in manipulation of deformable objects is typically conducted on a limited range of
scenarios, because handling each scenario on hardware takes significant effort. Realistic …

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its
application to the real-world robotics domain. Guiding the learning process with additional …

We consider the problem of inferring simulation parameters such that the behavior of an
object in simulation and the real world look similar. This real-to-sim problem is particularly …

By introducing randomness on the environments, domain randomization (DR) imposes
diversity to the policy training of deep reinforcement learning, and thus improves its …