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Reinforced Grounded Action Transformation for Sim-to-Real Transfer.
Haresh
Karnan, Siddharth Desai, Josiah P. Hanna, Garrett
Warnell, and Peter Stone.
In IEEE/RSJ International Conference on
Intelligent Robots and Systems(IROS 2020), October 2020.
14-minute video
presentation.
Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the “reality gap”). Some existing solutions to this sim-to-real problem, such as Grounded Action Transformation (GAT), use a small amount of real world experience to minimize the reality gap by “grounding” the simulator. While very effective in certain scenarios, GAT is not robust on problems that use complex function approximation techniques to model a policy. In this paper, we introduce Reinforced Grounded Action Transformation (RGAT), a new sim-to-real technique that uses Reinforcement Learning (RL) not only to update the target policy in simulation, but also to perform the grounding step itself. This novel formulation allows for end-to-end training during the grounding step, which, compared to GAT, produces a better grounded simulator. Moreover, we show experimentally in several MuJoCo domains that our approach leads to successful transfer for policies modeled using neural networks
@InProceedings{IROS20-Karnan,
author = {Haresh Karnan and Siddharth Desai and Josiah P. Hanna and Garrett Warnell and Peter Stone},
title = {Reinforced Grounded Action Transformation for Sim-to-Real Transfer},
abstract = {Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the “reality gapâ€). Some existing solutions to this sim-to-real problem, such as Grounded Action Transformation (GAT), use a small amount of real world experience to minimize the reality gap by “grounding†the simulator. While very effective in certain scenarios, GAT is not robust on problems that use complex function approximation techniques to model a policy. In this paper, we introduce Reinforced Grounded Action Transformation (RGAT), a new sim-to-real technique that uses Reinforcement Learning (RL) not only to update the target policy in simulation, but also to perform the grounding step itself. This novel formulation allows for end-to-end training during the grounding step, which, compared to GAT, produces a better grounded simulator. Moreover, we show experimentally in several MuJoCo domains that our approach leads to successful transfer for policies modeled using neural networks},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020)},
month = {October},
year = {2020},
location = {Las Vegas, NV, USA},
wwwnote={<a href="https://youtu.be/mInoJkzBP9M">14-minute video presentation</a>.},
}
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