… In this work, we introduce several reinforcement learning tasks with multiple commercially …
six reinforcement learning tasks based on three commercially available robots. Most of these …

… We use a UR5 robotic arm to define reinforcement learning tasks … learning agent and the
robot as well as determining all the aspects of the environment that define the learning task…

… The success of reinforcement learning for real world robotics … effort and oversight to enable
continuous learning. In this work, … learn dexterous robotic manipulation tasks in the real world, …

… the policy representation in robotics are identified. Three … reinforcement learning to real-world
robots are described: a pancake flipping task, a bipedal walking energy minimization task …

… in simulation, operate solely in a simulated world or are based on sequential or interval-…
the task. In this work we are interested instead in real-world, real-time collaborative learning …

… can effectively and efficiently learn control policies for real-world robots using the DRL …
training scenarios to richer repertoire tasks that are more common in real life. In addition, to …

… Reinforcement learning (RL) has proven its worth in a … task to present a testbed for other
researchers who wish to develop new algorithms that address the challenges of real world RL. …

… real-world robotics setting and are not often the focus of mainstream RL research. Our goal
is to provide a resource both for roboticists and machine learning … -task deep robotic learning …

… to learn how to learn. However, much of the current research on meta-reinforcement learning
focuses on task … For example, a commonly used meta-reinforcement learning benchmark …

… learning with multiple real-world robots to achieve better sample efficiency and generalization
performance on a door opening task using four robots. Another common approach is to …