The Embodied AI community has made significant strides in visual navigation tasks, exploring targets from 3D coordinates, objects, language descriptions, and images. However, these navigation models often handle only a single input modality as the target. With the progress achieved so far, it is time to move towards universal navigation models capable of handling various goal types, enabling more effective user interaction with robots. To facilitate this goal, we propose GOAT-Bench, a benchmark for the universal navigation task referred to as GO to AnyThing (GOAT). In this task, the agent is directed to navigate to a sequence of targets specified by the category name, language description, or image in an open-vocabulary fashion. We benchmark monolithic RL and modular methods on the GOAT task, analyzing their performance across modalities, the role of explicit and implicit scene memories, their robustness to noise in goal specifications, and the impact of memory in lifelong scenarios.

翻译：具身智能社区在视觉导航任务中取得了显著进展，探索了从三维坐标、物体、语言描述到图像的目标。然而，这些导航模型通常仅能处理单一输入模态作为目标。鉴于当前取得的成果，是时候向能够处理多种目标类型的通用导航模型迈进，以实现更高效的人机交互。为此，我们提出了GOAT-Bench，一个针对名为“前往任意物体”（GOAT）的通用导航任务的基准。在该任务中，智能体需要以开放词汇的方式，依次导航至由类别名称、语言描述或图像指定的目标序列。我们在GOAT任务上对单体强化学习与模块化方法进行了基准测试，分析了它们在不同模态下的性能、显式与隐式场景记忆的作用、对目标指定噪声的鲁棒性，以及终身场景中记忆的影响。