gym-depth-planning

Paper: link

To cite this paper:

  @Article{robotics11050109,
  AUTHOR = {Ugurlu, Halil Ibrahim and Pham, Xuan Huy and Kayacan, Erdal},
  TITLE = {Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots},
  JOURNAL = {Robotics},
  VOLUME = {11},
  YEAR = {2022},
  NUMBER = {5},
  ARTICLE-NUMBER = {109},
  URL = {https://www.mdpi.com/2218-6581/11/5/109},
  ISSN = {2218-6581},
  DOI = {10.3390/robotics11050109}
  }

Prerequisites

• Create conda environment using conda_env.yml
• pip install -e .

Examples

• Run roscore
• Open webots/worlds/train-no-dynamic-random-obstacles.wbt with Webots 2021a
• Training: run train/train.py
• Evaluation examples of pretrained models under eval folder