… [35] was used to … to this body of research, our goal is to use synthetic data to train networks
that detect complex, real-world objects. A similar approach to domain randomization is to …

… a one-shot deep neural network that is able to perform competitively against a state-of-the-art …
To our knowledge, this is the first deep network trained only on synthetic data that is able to …

… center the coordinate frame at the object centroid, and rotation was applied to … to avoid
initial penetration. The objects were then allowed to fall under the force of gravity, as well as to …

… We offer this dataset to the community to … to provide a dataset for perception researchers
to design and test their algorithms—we also want to support robotics researchers who need to …

We present an approach for estimating the pose of an external camera with respect to a robot
using a single RGB image of the robot. The image is processed by a deep neural network …

We present a system to infer and execute a human-readable program from a real-world
demonstration. The system consists of a series of neural networks to perform perception, …

… The goal of this work is to learn a policy in simulation (left) that transfers to the real world (…
The question we address in this work is whether it is possible to learn an end-to-end policy to …

… It is well-known that the structure of silicon carbide belongs to following four space … to rule
the properties (strain) of the silicon carbide nanopowders. Therefore, the aim of this study is to …

… the bigger surface to volume ratio may contribute to the more rapid onset of such an effect. …
pointing to reactions leading to oxygen and hydrogen species conversion to bridged hydroxils …

… systems to combat not only COVID-19 but also a spectrum of other infectious diseases. This
study is dedicated to … Additionally, the paper provides an up-to-date evaluation of the latest …