EmbodiedLGR：整合轻量级图表示与检索用于机器人代理中的语义-空间记忆
EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents
AI总结：
本文提出EmbodiedLGR-Agent，通过参数高效VLM构建语义-空间记忆，实现高效的环境记忆表示与检索，实验证明其在推理和查询时间上达到SOTA，且在实际部署中表现出良好的实用性。
摘要：
随着代理式人工智能应用于机器人领域的发展，需要能够高效构建和检索记忆与观察的代理日益增加。在复杂环境中运行的机器人必须构建记忆结构，以利用当前操作环境的mnemonic表示，从而实现有用的机器人-人类交互。与机器人交互的人可能期望机器人代理能够提供关于位置、事件或物体的信息，这要求代理在人类般的推理时间内提供精确答案，以被感知为响应。我们提出了Embodied Light Graph Retrieval Agent（EmbodiedLGR-Agent），一种由视觉-语言模型（VLM）驱动的代理架构，能够构建密集且高效的机器人操作环境表示。EmbodiedLGR-Agent通过参数高效的VLM构建混合的构建-检索方法，存储物体及其位置的低级信息在语义图中，同时保留传统检索增强架构下的场景高描述。EmbodiedLGR-Agent在流行的NaVQA数据集上进行了评估，在推理和查询时间上实现了SOTA性能，同时在全局任务的准确性上与当前SOTA方法具有竞争力。此外，EmbodiedLGR-Agent已成功部署在物理机器人上，在人机交互中展示了实际实用性，同时在本地运行视觉-语言模型和构建-检索流程。
英文摘要：
As the world of agentic artificial intelligence applied to robotics evolves, the need for agents capable of building and retrieving memories and observations efficiently is increasing. Robots operating in complex environments must build memory structures to enable useful human-robot interactions by leveraging the mnemonic representation of the current operating context. People interacting with robots may expect the embodied agent to provide information about locations, events, or objects, which requires the agent to provide precise answers within human-like inference times to be perceived as responsive. We propose the Embodied Light Graph Retrieval Agent (EmbodiedLGR-Agent), a visual-language model (VLM)-driven agent architecture that constructs dense and efficient representations of robot operating environments. EmbodiedLGR-Agent directly addresses the need for an efficient memory representation of the environment by providing a hybrid building-retrieval approach built on parameter-efficient VLMs that store low-level information about objects and their positions in a semantic graph, while retaining high-level descriptions of the observed scenes with a traditional retrieval-augmented architecture. EmbodiedLGR-Agent is evaluated on the popular NaVQA dataset, achieving state-of-the-art performance in inference and querying times for embodied agents, while retaining competitive accuracy on the global task relative to the current state-of-the-art approaches. Moreover, EmbodiedLGR-Agent was successfully deployed on a physical robot, showing practical utility in real-world contexts through human-robot interaction, while running the visual-language model and the building-retrieval pipeline locally.
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