In this work, we address generalization in targetdriven visual navigation by proposing a novel architecture composed by two networks, both exclusively trained in simulation. The first one has the objective of exploring the environment, while the other one of locating the target. We test our agent in both simulated and real scenarios, and validate its capabilities through extensive experiments with previously unseen goals and unknown mazes, even much larger than the ones used for training.

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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.

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