Continuous Viewpoint Planning in Conjunction with Dynamic Exploration for Active Object Recognition

Haibo Sun, Feng Zhu, Yanzi Kong, Jianyu Wang, Pengfei Zhao
2021 Entropy  
Active object recognition (AOR) aims at collecting additional information to improve recognition performance by purposefully adjusting the viewpoint of an agent. How to determine the next best viewpoint of the agent, i.e., viewpoint planning (VP), is a research focus. Most existing VP methods perform viewpoint exploration in the discrete viewpoint space, which have to sample viewpoint space and may bring in significant quantization error. To address this challenge, a continuous VP approach for
more » ... OR based on reinforcement learning is proposed. Specifically, we use two separate neural networks to model the VP policy as a parameterized Gaussian distribution and resort the proximal policy optimization framework to learn the policy. Furthermore, an adaptive entropy regularization based dynamic exploration scheme is presented to automatically adjust the viewpoint exploration ability in the learning process. To the end, experimental results on the public dataset GERMS well demonstrate the superiority of our proposed VP method.
doi:10.3390/e23121702 pmid:34946008 pmcid:PMC8701023 fatcat:tcmeltrchralrmntk2nlv2eqwa