We introduce a simple new method for visual imitation learning, which allows a novel robot
manipulation task to be learned from a single human demonstration, without requiring any …

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 …

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 …

Models trained with Deep Reinforcement learning (DRL) have been deployed in various
areas of robotics with varying degree of success. To overcome the limitations of data …

In small batch and customized production, human-robot collaborative assembly (HRCA) is
an important method to deal with the production demand of new-energy vehicles, which …

In recent years, domain randomization over dynamics parameters has gained a lot of
traction as a method for sim-to-real transfer of reinforcement learning policies in robotic …

Reinforcement Learning (RL) methods have been widely applied for robotic manipulations
via sim-to-real transfer, typically with proprioceptive and visual information. However, the …

Domain randomisation is a very popular method for visual sim-to-real transfer in robotics,
due to its simplicity and ability to achieve transfer without any real-world images at all …

Autonomous off-road driving is challenging as unsafe actions may lead to catastrophic
damage. As such, developing controllers in simulation is often desirable. However, robot …

Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability.
However, the potentially infinite number of Degrees of Freedom makes their modeling a …