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End-to-end Grasping Policies for Human-in-the-loop Robots via Deep Reinforcement Learning by Sharif, Mohammadreza; Erdogmus, Deniz; Amato, Christopher; Padir, Taskin is a scholarly article available to read on EtoBox.

What is End-to-end Grasping Policies for Human-in-the-loop Robots via Deep Reinforcement Learning about?

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 purpose we use Reinforcement Learning (RL) and Imitation Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation environment with real human trajectories that are augmented and selected using a Monte Carlo (MC) simulation method. We also offer a success model which once trained on the expert policy data and the RL policy roll-out transitions, can provide transparency to how the deep policy works and when it is probably going to fail.

Author

Sharif, Mohammadreza; Erdogmus, Deniz; Amato, Christopher; Padir, Taskin

Published

2021

Language

EN