AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2007.06343v2
• Date: Sat, 1 Aug 2020 11:10:52 GMT
• Title: AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning
• Authors: Rahul Tallamraju, Nitin Saini, Elia Bonetto, Michael Pabst, Yu Tang Liu, Michael J. Black and Aamir Ahmad
• Abstract summary: 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 a single moving person using multiple aerial vehicles.
• Score: 38.429105809093116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Video Link: https://bit.ly/38SJfjo Supplementary: https://bit.ly/3evfo1O

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