Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can adapt to environments and detect interesting scenes quickly, but lack the ability to adapt to human-informed interesting objects. To solve this problem, we introduce a human-interactive framework, AirInteraction, that can detect human-informed objects via few-shot online learning. To reduce the communication bandwidth, we first apply an online unsupervised learning algorithm on the unmanned vehicle for interestingness recognition and then only send the potential interesting scenes to a base-station for human inspection. The human operator is able to draw and provide bounding box annotations for particular interesting objects, which are sent back to the robot to detect similar objects via few-shot learning. Only using few human-labeled examples, the robot can learn novel interesting object categories during the mission and detect interesting scenes that contain the objects. We evaluate our method on various interesting scene recognition datasets. To the best of our knowledge, it is the first human-informed few-shot object detection framework for autonomous exploration.
翻译:有趣的认识对于自主探索移动机器人的决策至关重要。 先前的方法提出了一种不受监督的在线学习方法,可以快速适应环境,探测有趣的场景,但缺乏适应人类知情的有趣天体的能力。 为了解决这个问题,我们引入了一个人类互动框架,即空气互动,可以通过微小的在线学习来检测人类知情天体。为了减少通信带宽,我们首先在无人驾驶飞行器上应用一个不受监督的在线学习算法,以了解有趣的程度,然后将潜在有趣的场景发送到一个用于人类检查的基站。 人类操作者能够为特定有趣的天体绘制并提供捆绑式插图,这些天体被发回机器人,以便通过少许的光谱学习来探测类似天体。 仅使用少量人类标签的例子,机器人就可以在飞行任务中学习新的有趣天体分类,并探测含有天体的有趣场景。 我们评估了各种有趣的场景识别数据集的方法。 据我们所知,这是用于自主探索的首个人类知情的少发天体物体探测框架。