Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require fast deployment with little or no training data. Prior work considers "interestingness" based on data from the same distribution. Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly. To address this problem, we develop a novel translation-invariant visual memory and design a three-stage architecture for long-term, short-term, and online learning, which enables the system to learn human-like experience, environmental knowledge, and online adaption, respectively. With this system, we achieve an average of 20% higher accuracy than the state-of-the-art unsupervised methods in a subterranean tunnel environment. We show comparable performance to supervised methods for robot exploration scenarios showing the efficacy of our approach. We expect that the presented method will play an important role in the robotic interestingness recognition exploration tasks.
翻译:自主机器人经常需要检测“ 感兴趣的” 场景, 以决定进一步探索, 或者决定哪些数据可以共享。 这些场景往往需要快速部署, 且很少或根本没有培训数据。 先前的工作考虑基于同一分布的数据的“ 兴趣 ” 。 相反, 我们提议开发一种方法, 自动调整在线环境以快速报告有趣的场景 。 为了解决这个问题, 我们开发了一个新的翻译动因的视觉记忆, 为长期、 短期和在线学习设计了一个三阶段结构, 使系统能够分别学习人的经验、 环境知识 和在线适应 。 有了这个系统, 我们实现的精度平均比亚地球隧道环境中最先进的不受监督的方法高20% 。 我们展示了类似的性能, 来监督机器人探索情景的方法, 展示我们的方法的有效性。 我们期望所展示的方法将在机器人有趣的探索任务中发挥重要作用 。