  Active sensing and planning in unknown, cluttered environments is an open
challenge for robots intending to provide home service, search and rescue,
narrow-passage inspection, and medical assistance. Although many active sensing
methods exist, they often consider open spaces, assume known settings, or
mostly do not generalize to real-world scenarios. We present the active neural
sensing approach that generates the kinematically feasible viewpoint sequences
for the robot manipulator with an in-hand camera to gather the minimum number
of observations needed to reconstruct the underlying environment. Our framework
actively collects the visual RGBD observations, aggregates them into scene
representation, and performs object shape inference to avoid unnecessary robot
interactions with the environment. We train our approach on synthetic data with
domain randomization and demonstrate its successful execution via sim-to-real
transfer in reconstructing narrow, covered, real-world cabinet environments
cluttered with unknown objects. The natural cabinet scenarios impose
significant challenges for robot motion and scene reconstruction due to
surrounding obstacles and low ambient lighting conditions. However, despite
unfavorable settings, our method exhibits high performance compared to its
baselines in terms of various environment reconstruction metrics, including
planning speed, the number of viewpoints, and overall scene coverage.
