End-to-end grasping policies for human-in-the-loop robots via deep
reinforcement learning
- URL: http://arxiv.org/abs/2104.12842v1
- Date: Mon, 26 Apr 2021 19:39:23 GMT
- Title: End-to-end grasping policies for human-in-the-loop robots via deep
reinforcement learning
- Authors: Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato, and Taskin
Padir
- Abstract summary: State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromy robustness (EMG) inference issues.
We present a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories.
- Score: 24.407804468007228
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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