OpenIN：动态家庭环境中的面向实例的开放词汇导航
OpenIN: Open-Vocabulary Instance-Oriented Navigation in Dynamic Domestic Environments
AI总结：
针对动态家庭中同类别多实例目标位置和承载物变化的问题，本文提出OpenIN，利用可动态更新的开放词汇CRSG与结合LLM常识和视觉-语言相似度的MDP策略实现实例导航。
摘要：
在日常家庭场景中，杯子等常用物体往往具有不固定的位置，并且同一类别内存在多个实例，其承载物也会频繁变化。因此，机器人要高效导航到特定实例变得具有挑战性。为应对这一挑战，机器人必须持续捕捉并更新场景变化与规划。然而，当前的物体导航方法主要关注语义层面，缺乏动态更新场景表示的能力。相比之下，本文捕捉常用物体与其静态承载物之间的关系，构建开放词汇的承载关系场景图（Carrier-Relationship Scene Graph，CRSG），并在机器人导航过程中更新承载状态，以反映场景的动态变化。在CRSG基础上，我们进一步提出一种实例导航策略，将导航过程建模为马尔可夫决策过程。在每一步，决策均由大语言模型的常识知识和视觉-语言特征相似性提供依据。我们在Habitat模拟器中为日常常用物品设计了一系列长序列导航任务。结果表明，通过更新CRSG，机器人能够高效导航到被移动的目标。此外，我们将所提算法部署到真实机器人上，验证了其实际有效性。项目页面见：https://OpenIN-nav.github.io。
英文摘要：
In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot must capture and update scene changes and plans continuously. However, current object navigation approaches primarily focus on the semantic level and lack the ability to dynamically update scene representation. In contrast, this paper captures the relationships between frequently used objects and their static carriers. It constructs an open-vocabulary Carrier-Relationship Scene Graph (CRSG) and updates the carrying status during robot navigation to reflect the dynamic changes of the scene. Based on the CRSG, we further propose an instance navigation strategy that models the navigation process as a Markov Decision Process. At each step, decisions are informed by the Large Language Model's commonsense knowledge and visual-language feature similarity. We designed a series of long-sequence navigation tasks for frequently used everyday items in the Habitat simulator. The results demonstrate that by updating the CRSG, the robot can efficiently navigate to moved targets. Additionally, we deployed our algorithm on a real robot and validated its practical effectiveness. The project page can be found here: https://OpenIN-nav.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
