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The presentation and analysis of image data from a single viewpoint are oftennot sufficient to solve a task. Several viewpoints are necessary to obtain moreinformation. The next-best-view problem attempts to find the optimal viewpointwith the greatest information gain for the underlying task. In this work, arobot arm holds an object in its end-effector and searches for a sequence ofnext-best-view to explicitly identify the object. We use Soft Actor-Critic(SAC), a method of deep reinforcement learning, to learn these next-best-viewsfor a specific set of objects. The evaluation shows that an agent can learn todetermine an object pose to which the robot arm should move an object. Thisleads to a viewpoint that provides a more accurate prediction to distinguishsuch an object from other objects better. We make the code publicly availablefor the scientific community and for reproducibility.

Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification