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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Improving the Robustness of Reinforcement Learning Policies With L1 Adaptive Control
A reinforcement learning (RL) control policy could fail in a new/perturbed environment that is different from the
training environment, due to the presence of dynamic variations. For controlling systems with continuous state and action spaces, we propose an add-on approach to robustifying a pre-trained RL policy by augmenting it with an L1 adaptive controller (L1AC). Leveraging the capability of an L1AC for fast estimation and active ompensation of dynamic variations, the proposed approach can improve the robustness of an RL policy which is trained either in a simulator or in the real world without consideration of a broad class of dynamic variations. Numerical and real-world experiments empirically demonstrate the efficacy of the proposed approach in
robustifying RL policies trained using both model-free and modelbased methods.
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- Award ID(s):
- 1932529
- PAR ID:
- 10482819
- Editor(s):
- Tamim Asfour, editor in
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE robotics automation letters
- ISSN:
- 2377-3766
- Subject(s) / Keyword(s):
- Reinforcement learning, machine learning for robot control, robust/adaptive control, robot safety
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
