论文标题
通过无监督在线学习的机器人有趣性的视觉记忆力
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
论文作者
论文摘要
在本文中，我们探讨了移动机器人有趣场景预测的问题。目前，该领域尚未得到充分展望，但对于许多实际应用至关重要，例如自主探索和决策。受工业需求的启发，我们首先提出了一种新颖的翻译视觉记忆，用于回忆和识别有趣的场景，然后设计长期，短期和在线学习的三阶段架构。这使我们的系统能够分别学习类似人类的体验，环境知识和在线适应。在挑战机器人有趣的数据集上，我们的方法比最新的算法获得了更高的准确性。
In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as autonomous exploration and decision making. Inspired by industrial demands, we first propose a novel translation-invariant visual memory for recalling and identifying interesting scenes, then design a three-stage architecture of long-term, short-term, and online learning. This enables our system to learn human-like experience, environmental knowledge, and online adaption, respectively. Our approach achieves much higher accuracy than the state-of-the-art algorithms on challenging robotic interestingness datasets.
