The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

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 …

Deep reinforcement learning is a promising approach to learning policies in uncontrolled
environments that do not require domain knowledge. Unfortunately, due to sample …

Legged robots are physically capable of traversing a wide range of challenging
environments, but designing controllers that are sufficiently robust to handle this diversity …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

Training control policies in simulation is more appealing than on real robots directly, as it
allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably …

This paper introduces the task of Planar Robot Casting (PRC): where one planar motion of a
robot arm holding one end of a cable causes the other end to slide across the plane toward …

A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state
representation using self-supervised methods, which has the potential benefit of improved …

Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …

On robotics computer vision tasks, generating and annotating large amounts of data from
real-world for the use of deep learning-based approaches is often difficult or even …