Selfie Drone Stick: A Natural Interface for Quadcopter Photography
This addresses the difficulty for users in manually controlling quadcopters for photography, offering an incremental improvement through a more intuitive interface and training methods.
The paper tackles the problem of teleoperating quadcopters for photography by introducing the SelfieDroneStick, a natural interface that uses smartphone sensors to guide the quadcopter autonomously to optimal vantage points for taking long-range selfies, with innovations in deep reinforcement learning enabling successful transfer from simulation to real hardware.
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 robot: 1) an abstract state representation for transferring learning from simulation to the hardware platform, and 2) reward shaping and staging paradigms for training the controller. Both of these improvements were found to be essential in learning a robot controller from simulation that transfers successfully to the real robot.