Bridging Reality Gap Between Virtual and Physical Robot through Domain Randomization and Induced Noise
Mahesh Ranaweera, Qusay H. Mahmoud · 2022
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.