Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently with camera sensors only. Existing navigation methods mainly focus on fixed cameras and few attempts have been made to navigate with active cameras. As a result, the agent may take a very long time to perceive the environment due to limited camera scope. In contrast, humans typically gain a larger field of view by looking around for a better perception of the environment. How to make robots perceive the environment as efficiently as humans is a fundamental problem in robotics. In this paper, we consider navigating to multiple objects more efficiently with active cameras. Specifically, we cast moving camera to a Markov Decision Process and reformulate the active camera problem as a reinforcement learning problem. However, we have to address two new challenges: 1) how to learn a good camera policy in complex environments and 2) how to coordinate it with the navigation policy. To address these, we carefully design a reward function to encourage the agent to explore more areas by moving camera actively. Moreover, we exploit human experience to infer a rule-based camera action to guide the learning process. Last, to better coordinate two kinds of policies, the camera policy takes navigation actions into account when making camera moving decisions. Experimental results show our camera policy consistently improves the performance of multi-object navigation over four baselines on two datasets.
翻译:在机器人应用中,让机器人自动导航到多个物体是必要的,但在机器人应用中困难重重。 关键的挑战之一是如何以摄像传感器有效探索环境。 现有的导航方法主要侧重于固定的相机,没有多少尝试使用活跃的相机进行导航。 结果,由于摄像范围有限,代理可能花费很长的时间来感知环境。 相比之下, 人类通常通过环顾四周寻找对环境的更好认识而获得更大的视野。 如何让机器人像人类一样高效地看待环境是机器人中的一个基本问题。 在本文中, 我们考虑用活跃的相机更高效地浏览多天体。 具体地说, 我们把相机投放到Markov 决策程序上, 并重新配置活跃的相机问题作为强化学习问题。 然而, 我们不得不应对两个新的挑战:(1) 如何在复杂的环境中学习良好的相机政策, 以及(2) 如何与导航政策协调。 为了解决这些问题, 我们仔细设计了一个奖励功能, 以鼓励代理人通过积极移动相机来探索更多的区域。 此外, 我们利用人类的经验来推断基于规则的相机行动来引导学习进程。 最终, 我们把相机的相机应用两个实验性实验性实验性决定 。