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

Ranaweera, Mahesh; Mahmoud, Qusay H.

doi:10.21428/594757db.09aa0c75

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…This framework was utilized to facilitate the research conducted by the authors. The framework was utilized to evaluate techniques for Sim2Real RL [30] and to show the effectiveness of DR and induced noise to bridge the reality gap in [2]. Two DRL algorithms were used for the evaluation: Actor-Critic and Deep Q-Learning.…”

Section: Resultsmentioning

confidence: 99%

“…However, the success of DRL methods is often hindered by a lack of quality or sufficient training data (sample inefficiency), particularly when the tasks involve real-world scenarios and applications. Developing advanced robotic systems in the real world is often challenging because the required training data can be dangerous or difficult to gather [2]. Therefore, creating virtual environments for Sim2Real transfer is important for offering a safe, scalable, and cost-effective platform for training, testing, and evaluating intelligent systems while minimizing the risks and costs associated with real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning with Godot Game Engine

Ranaweera,

Mahmoud

2024

Electronics

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This paper introduces a Python framework for developing Deep Reinforcement Learning (DRL) in an open-source Godot game engine to tackle sim-to-real research. A framework was designed to communicate and interface with the Godot game engine to perform the DRL. With the Godot game engine, users will be able to set up their environment while defining the constraints, motion, interactive objects, and actions to be performed. The framework interfaces with the Godot game engine to perform defined actions. It can be further extended to perform domain randomization and enhance overall learning by increasing the complexity of the environment. Unlike other proprietary physics or game engines, Godot provides extensive developmental freedom under an open-source licence. By incorporating Godot’s built-in powerful node-based environment system, flexible user interface, and the proposed Python framework, developers can extend its features to develop deep learning applications. Research performed on Sim2Real using this framework has provided great insight into the factors that affect the gap in reality. It also demonstrated the effectiveness of this framework in Sim2Real applications and research.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning with Godot Game Engine

Ranaweera,

Mahmoud

2024

Electronics

Self Cite

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“…In this research, Gaussian noise was utilized to add a randomly-sampled noise into the action space and to add noise to the raw input image data during the RL. The goal of adding noise was to improve the generalization error [34] and to compensate for noise in hardware sensors, gear backslash, and latency [35].…”

Section: E Added Noisementioning

confidence: 99%

Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

Ranaweera

Mahmoud

2023

IEEE Access

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Creating Reinforcement learning(RL) agents that can perform tasks in the real-world robotic systems remains a challenging task due to inconsistencies between the virtual-and the real-world. This is known as the ''reality-gap'' which hinders the performance of a RL agent trained in a virtual environment. The research describes the techniques used to train the models, generate randomized environments, reward function, and techniques utilized to transfer the model to the physical environment for evaluation. For this investigation, a low-cost 3-degrees-of-freedom (DOF) Steward platform was 3D modeled and created virtually and physically. The goal of the 3D-Stewart platform was to guide and balance the marble towards the center. Custom end-to-end APIs were developed to interact with the Godot game engine, manipulate physics and dynamics, interact with the in-game lighting and perform environment randomizations. Two RL algorithms: Q-learning and Actor-Critic, were implemented to evaluate the performance by using domain randomization and induced noise to bridge the reality gap. For Q-learning, raw frames were used to make predictions while Actor-Critic utilized marble position, velocity vector and relative position by preprocessing captured frames. The experimental results show the effectiveness of domain randomization and introduction of noise during the training.

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

Cited by 2 publications

References 18 publications

Deep Reinforcement Learning with Godot Game Engine

Deep Reinforcement Learning with Godot Game Engine

Bridging the Reality Gap Between Virtual and Physical Environments Through Reinforcement Learning

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