In this article, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning (RL), to supply adaptive assistance during a robot-assisted handwriting rehabilitation task. Unlike previous AAN controllers, our method does not rely on patient-specific controller parameters or physical models. We propose the use of a virtual … [Read more...] about AR3n: A Reinforcement Learning-Based Assist-as-Needed Controller for Robotic Rehabilitation
Robot motion
Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World
Tumbling robots are simple platforms that are able to traverse large obstacles relative to their size, at the cost of being difficult to control. Existing control methods apply only a subset of possible robot motions and make the assumption of flat terrain. Reinforcement learning (RL) allows for the development of sophisticated control schemes that can adapt to diverse … [Read more...] about Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World
