Robot training in virtual environments using Reinforcement Learning techniques

  • Natália Souza Soares UFPE
  • João Marcelo Xavier Natário Teixeira UFPE
  • Veronica Teichrieb UFPE


In this work, we propose a framework to train a robot in a virtual environment using Reinforcement Learning (RL) techniques and thus facilitating the use of this type of approach in robotics. With our integrated solution for virtual training, it is possible to programmatically change the environment parameters, making it easy to implement domain randomization techniques on-the-fly. We conducted experiments with a TurtleBot 2i in an indoor navigation task with static obstacle avoidance using an RL algorithm called Proximal Policy Optimization (PPO). Our results show that even though the training did not use any real data, the trained model was able to generalize to different virtual environments and real-world scenes.
Palavras-chave: Reinforcement Learning, Robotics, Virtual Environments, Simulation


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SOARES, Natália Souza; TEIXEIRA, João Marcelo Xavier Natário; TEICHRIEB, Veronica. Robot training in virtual environments using Reinforcement Learning techniques. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO DE REALIDADE VIRTUAL E AUMENTADA (SVR), 22. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 25-29. DOI: