  Picking a specific object from clutter is an essential component of many
manipulation tasks. Partial observations often require the robot to collect
additional views of the scene before attempting a grasp. This paper proposes a
closed-loop next-best-view planner that drives exploration based on occluded
object parts. By continuously predicting grasps from an up-to-date scene
reconstruction, our policy can decide online to finalize a grasp execution or
to adapt the robot's trajectory for further exploration. We show that our
reactive approach decreases execution times without loss of grasp success rates
compared to common camera placements and handles situations where the fixed
baselines fail. Video and code are available at
https://github.com/ethz-asl/active_grasp.
