  In this letter, we introduce a deep reinforcement learning (RL) 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-linearity 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. Video Link:
https://bit.ly/38SJfjo Supplementary: https://bit.ly/3evfo1O
