  Active recognition enables robots to intelligently explore novel
observations, thereby acquiring more information while circumventing undesired
viewing conditions. Recent approaches favor learning policies from simulated or
collected data, wherein appropriate actions are more frequently selected when
the recognition is accurate. However, most recognition modules are developed
under the closed-world assumption, which makes them ill-equipped to handle
unexpected inputs, such as the absence of the target object in the current
observation. To address this issue, we propose treating active recognition as a
sequential evidence-gathering process, providing by-step uncertainty
quantification and reliable prediction under the evidence combination theory.
Additionally, the reward function developed in this paper effectively
characterizes the merit of actions when operating in open-world environments.
To evaluate the performance, we collect a dataset from an indoor simulator,
encompassing various recognition challenges such as distance, occlusion levels,
and visibility. Through a series of experiments on recognition and robustness
analysis, we demonstrate the necessity of introducing uncertainties to active
recognition and the superior performance of the proposed method.
