End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning
Mohammadreza Sharif Deniz Erdogmus Christopher Amato Taskin Padir Mohammadreza Sharif Deniz Erdogmus Christopher Amato Taskin Padir
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into integrating EMG with other signals, often in an ad hoc manner. In this paper, we are presenting a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories. For this purpos...