The presentation and analysis of image data from a single viewpoint are often not sufficient to solve a task. Several viewpoints are necessary to obtain more information. The next-best-view problem attempts to find the optimal viewpoint with the greatest information gain for the underlying task. In this work, a robot arm holds an object in its end-effector and searches for a sequence of next-best-view to explicitly identify the object. We use Soft Actor-Critic (SAC), a method of deep reinforcement learning, to learn these next-best-views for a specific set of objects. The evaluation shows that an agent can learn to determine an object pose to which the robot arm should move an object. This leads to a viewpoint that provides a more accurate prediction to distinguish such an object from other objects better. We make the code publicly available for the scientific community and for reproducibility.
@article{arxiv.2110.06766,
title = {Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification},
author = {Christian Korbach and Markus D. Solbach and Raphael Memmesheimer and Dietrich Paulus and John K. Tsotsos},
journal= {arXiv preprint arXiv:2110.06766},
year = {2021}
}