  Tracking a target in cluttered and dynamic environments is challenging but
forms a core component in applications like aerial cinematography. The
obstacles in the environment not only pose collision risk but can also occlude
the target from the field-of-view of the robot. Moreover, the target future
trajectory may be unknown and only its current state can be estimated. In this
paper, we propose a learned probabilistic neural policy for safe,
occlusion-free target tracking.
  The core novelty of our work stems from the structure of our policy network
that combines generative modeling based on Conditional Variational Autoencoder
(CVAE) with differentiable optimization layers. The role of the CVAE is to
provide a base trajectory distribution which is then projected onto a learned
feasible set through the optimization layer. Furthermore, both the weights of
the CVAE network and the parameters of the differentiable optimization can be
learned in an end-to-end fashion through demonstration trajectories.
  We improve the state-of-the-art (SOTA) in the following respects. We show
that our learned policy outperforms existing SOTA in terms of
occlusion/collision avoidance capabilities and computation time. Second, we
present an extensive ablation showing how different components of our learning
pipeline contribute to the overall tracking task. We also demonstrate the
real-time performance of our approach on resource-constrained hardware such as
NVIDIA Jetson TX2. Finally, our learned policy can also be viewed as a reactive
planner for navigation in highly cluttered environments.
