AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning
Video Attached with IROS + RAL 2020 accepted article.
AirCapRL Project Page: https://ps.is.tuebingen.mpg.de/resear...
Manuscript Details:
*Title*: AirCapRL: Autonomous Aerial Human Motion Captureusing Deep Reinforcement Learning
*Authors*: Rahul Tallamraju, Nitin Saini, Elia Bonetto, Michael Pabst, Yu Tang Liu, Michael J. Black and Aamir Ahmad
*Affiliation*: Authors are with the Max-Planck Institute for Intelligent Systems, Tübingen, Germany.
*Abstract*: 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.