Active search and coverage using point-cloud reinforcement learning
Matthias Rosynski Alexandru Pop Lucian Busoniu
We consider a problem in which the trajectory of a mobile 3D sensor must be
optimized so that certain objects are both found in the overall scene and
covered by the point cloud, as fast as possible. This problem is called target
search and coverage, and the paper provides an end-to-end deep reinforcement
learning (RL) solution to solve it. The deep neural network combines four
components: deep hierarchical feature learning occurs in the first stage,
followed by multi-head transformers in the second, max-pooling and merging with
bypassed information to preserve spatial relationships in the third, and a
distributional dueling network in the last stage. To evaluate the method, a
simulator is developed where cylinders must be found by a Kinect sensor. A
network architecture study shows that deep hierarchical feature learning works
for RL and that by using farthest point sampling (FPS) we can reduce the amount
of points and achieve not only a reduction of the network size but also better
results. We also show that multi-head attention for point-clouds helps to learn
the agent faster but converges to the same outcome. Finally, we compare RL
using the best network with a greedy baseline that maximizes immediate rewards
and requires for that purpose an oracle that predicts the next observation. We
decided RL achieves significantly better and more robust results than the
greedy strategy.