Analysis of Different Methods to Close the Reality Gap for Instance Segmentation in a Flexible Assembly Cell

Hafez, Nawal; Dietrich, Vincent; Roehrl, Stefan

doi:10.1007/978-3-030-48989-2_54

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2022

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“…There are few methods proposed by researchers to improve the quality of transfer to close the reality gap. Some of the proposed methods are photo-realism rendering (realistic virtual environment), domain randomization and domain adaptation [6].…”

Section: Related Workmentioning

confidence: 99%

Bridging Reality Gap Between Virtual and Physical Robot through Domain Randomization and Induced Noise

Ranaweera¹,

Mahmoud²

2022

Proceedings of the Canadian Conference on Artificial Intelligence

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This paper investigates techniques that can be utilized to bridge the reality gap between virtual and physical robots, by implementing a virtual environment and a physical robotic platform to evaluate the robustness of transfer learning from virtual to real-world robots. The proposed approach utilizes two reinforcement (RL) learning methods: deep Q-learning and Actor-Critic methodology to create a model that can learn from a virtual environment and performs in a physical environment. Techniques such as domain randomization and induced noise during training were utilized to bring variability and ultimately improve the learning policies. The experimental results demonstrate the effectiveness of the Actor-Critic reinforcement learning technique to bridge the reality gap.

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Section: Related Workmentioning

confidence: 99%