The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e.g., 158% relative Success Rate improvement than CoW on MP3D).

翻译：准确定位并导航至特定目标物体，是现实世界中与物体交互完成任务的具身智能体的关键能力。此类目标导航任务通常需要在带有标注物体的视觉环境中进行大规模训练，这导致其对未知环境中的新物体泛化能力较差。本文提出一种新颖的零样本目标导航方法——基于软常识约束的探索（ESC），该方法将预训练模型中的常识知识迁移至开放世界目标导航，无需任何导航经验或视觉环境训练。首先，ESC利用预训练视觉语言模型进行开放世界提示引导的目标定位，并借助预训练常识语言模型进行房间与物体推理。随后，ESC将常识知识转化为导航动作，通过建模为软逻辑谓词实现高效探索。在MP3D、HM3D和RoboTHOR基准上的大量实验表明，ESC方法相较基线取得显著提升，并在零样本目标导航任务中达到最新最优性能（例如在MP3D数据集上，相较于CoW方法实现了158%的相对成功率提升）。