TL;DR
This paper introduces SelfieDroneStick, a natural interface that enables users to guide quadcopters to optimal vantage points for photography using smartphone sensors, combining deep reinforcement learning with simulation for real-world deployment.
Contribution
It presents a novel interface for quadcopter photography and advances deep reinforcement learning techniques for transferring control policies from simulation to real robots.
Findings
Successful transfer of deep RL models from simulation to real quadcopters
Effective abstract state representation for sim-to-real transfer
Training paradigms that improve controller performance in real-world deployment
Abstract
A physical selfie stick extends the user's reach, enabling the acquisition of personal photos that include more of the background scene. Similarly, a quadcopter can capture photos from vantage points unattainable by the user; but teleoperating a quadcopter to good viewpoints is a difficult task. This paper presents a natural interface for quadcopter photography, the SelfieDroneStick that allows the user to guide the quadcopter to the optimal vantage point based on the phone's sensors. Users specify the composition of their desired long-range selfies using their smartphone, and the quadcopter autonomously flies to a sequence of vantage points from where the desired shots can be taken. The robot controller is trained from a combination of real-world images and simulated flight data. This paper describes two key innovations required to deploy deep reinforcement learning models on a real…
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