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.

翻译：主动识别使机器人能够智能地探索新颖观测，从而在规避不良视角的同时获取更多信息。近期方法倾向于从仿真或采集数据中学习策略，当识别准确时能更频繁地选择恰当动作。然而，多数识别模块基于封闭世界假设开发，难以处理意外输入（如当前观测中缺失目标物体）。针对该问题，本文提出将主动识别视为序贯证据收集过程，在证据组合理论框架下实现逐步骤的不确定性量化与可靠预测。此外，本文设计的奖励函数有效刻画了开放世界环境下不同动作的优劣性能。为评估表现，我们从室内仿真器中采集数据集，涵盖距离、遮挡程度及可见度等多种识别挑战。通过一系列识别性能与鲁棒性分析实验，我们证明了在主动识别中引入不确定性的必要性，并验证了所提方法的优越性能。