  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.
