Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.). We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e.g. fruits, glasses, phones, etc.) that frequently change their positions due to human intervention. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability.
翻译:物体-目标导航(Object-nav)意味着搜索、识别和导航目标对象。对象-导航(Object-nav)已经由Embodied-AI社区进行了广泛的研究,但大多数解决办法往往局限于考虑静态物体(例如电视、冰箱等)。我们为物体-目标导航提出了一个模块框架,不仅能够高效率地搜索静态物体的室内环境,而且能够搜索经常因人类干预而改变其位置的移动物体(例如水果、眼镜、电话等)。我们的背景带代理在面对不确定性时展示乐观态度,从而有效地探索了环境,并学习了从每个导航地点发现不同物体的可能性模型。这些可能性被作为加权最小拉特度解算器的奖励,用以推断机器人的轨迹。我们评估了在两个模拟环境中和真实世界环境中的算法,以显示高采样效率和可靠性。