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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