Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception

• URL: http://arxiv.org/abs/2311.13793v1
• Date: Thu, 23 Nov 2023 03:51:46 GMT
• Title: Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception
• Authors: Lei Fan, Mingfu Liang, Yunxuan Li, Gang Hua and Ying Wu
• Abstract summary: Active recognition enables robots to explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions. 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. We propose treating active recognition as a sequential evidence-gathering process, providing by-step uncertainty and reliable prediction under the evidence combination theory.
• Score: 21.639429724987902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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