Existing object-search approaches enable robots to search through free pathways, however, robots operating in unstructured human-centered environments frequently also have to manipulate the environment to their needs. In this work, we introduce a novel interactive multi-object search task in which a robot has to open doors to navigate rooms and search inside cabinets and drawers to find target objects. These new challenges require combining manipulation and navigation skills in unexplored environments. We present HIMOS, a hierarchical reinforcement learning approach that learns to compose exploration, navigation, and manipulation skills. To achieve this, we design an abstract high-level action space around a semantic map memory and leverage the explored environment as instance navigation points. We perform extensive experiments in simulation and the real-world that demonstrate that HIMOS effectively transfers to new environments in a zero-shot manner. It shows robustness to unseen subpolicies, failures in their execution, and different robot kinematics. These capabilities open the door to a wide range of downstream tasks across embodied AI and real-world use cases.

翻译：现有的目标搜索方法使机器人能够通过自由通道进行搜索，然而，在非结构化的人类中心环境中运行的机器人通常还需要根据需求操纵环境。在这项工作中，我们提出了一种新型交互式多目标搜索任务，其中机器人必须开门进入房间，并搜索柜子和抽屉以找到目标物体。这些新挑战要求机器人能够在未知环境中结合操作与导航技能。我们提出了 HIMOS，一种层次化强化学习方法，通过学习组合探索、导航和操作技能。为实现这一目标，我们围绕语义地图记忆设计了一个抽象的高层动作空间，并利用已探索的环境作为实例化导航点。我们在仿真和真实世界中进行了大量实验，结果表明 HIMOS 能够以零样本方式有效迁移到新环境。该方法对未见过的子策略、执行失败以及不同的机器人运动学表现出鲁棒性。这些能力为具身智能及现实世界中的广泛应用场景打开了大门。