  Visual active tracking is a growing research topic in robotics due to its key
role in applications such as human assistance, disaster recovery, and
surveillance. In contrast to passive tracking, active tracking approaches
combine vision and control capabilities to detect and actively track the
target. Most of the work in this area focuses on ground robots, while the very
few contributions on aerial platforms still pose important design constraints
that limit their applicability. To overcome these limitations, in this paper we
propose D-VAT, a novel end-to-end visual active tracking methodology based on
deep reinforcement learning that is tailored to micro aerial vehicle platforms.
The D-VAT agent computes the vehicle thrust and angular velocity commands
needed to track the target by directly processing monocular camera
measurements. We show that the proposed approach allows for precise and
collision-free tracking operations, outperforming different state-of-the-art
baselines on simulated environments which differ significantly from those
encountered during training. Moreover, we demonstrate a smooth real-world
transition to a quadrotor platform with mixed-reality.
