@article{aircaprl,
  title = {AirCapRL: Autonomous Aerial Human Motion Capture Using Deep Reinforcement Learning},
  journal = {IEEE Robotics and Automation Letters},
  abstract = {In this letter, we introduce a deep reinforcement learning (DRL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap). We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose, and shape of a single moving person using multiple micro aerial vehicles. State-of-the-art solutions to this problem are based on classical control methods, which depend on hand-crafted system, and observation models. Such models are difficult to derive, and generalize across different systems. Moreover, the non-linearities, and non-convexities of these models lead to sub-optimal controls. In our work, we formulate this problem as a sequential decision making task to achieve the vision-based motion capture objectives, and solve it using a deep neural network-based RL method. We leverage proximal policy optimization (PPO) to train a stochastic decentralized control policy for formation control. The neural network is trained in a parallelized setup in synthetic environments. We performed extensive simulation experiments to validate our approach. Finally, real-robot experiments demonstrate that our policies generalize to real world conditions.},
  volume = {5},
  number = {4},
  pages = {6678--6685},
  publisher = {IEEE},
  month = oct,
  year = {2020},
  note = {Also accepted and presented in the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).},
  slug = {aircaprl},
  author = {Tallamraju, Rahul and Saini, Nitin and Bonetto, Elia and Pabst, Michael and Liu, Yu Tang and Black, Michael and Ahmad, Aamir},
  url = {https://ieeexplore.ieee.org/document/9158379},
  month_numeric = {10}
}