EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents

• URL: http://arxiv.org/abs/2604.18271v1
• Date: Mon, 20 Apr 2026 13:45:54 GMT
• Title: EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents
• Abstract summary: EmbodiedLGR-Agent is a visual-language model (VLM)-driven agent architecture that constructs dense and efficient representations of robot operating environments.<n>EmbodiedLGR-Agent is evaluated on the popular NaVQA dataset, achieving state-of-the-art performance in inference and querying times for embodied agents.
• Score: 7.142235510048155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Robot Data Factory [35.46679263015648]
  We introduce the Robot Data Factory (RDF), a mission-driven infrastructure for continuously generating, validating, benchmarking, and reusing experience.<n>RDF organizes heterogeneous robots and environment-specific training grounds through reproducible missions, skill curricula, synchronized multimodal sensing, external ground truth, an agentic robot network, data pipelines, and living benchmarks.<n>We further formalize robot experience and its quality, introduce a mission-task-skill-episode-dataset-benchmark-capability hierarchy, and derive quantitative scaling laws and an algorithmic procedure connecting robot fleet size, sensor rates, storage, learning representations, tokenization, training compute
  arXiv  Detail & Related papers  (2026-09-15T06:30:11Z)
• Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF [0.0]
  In cognitive robotics, semantically rich representations are required to enable explainable reasoning.<n>We present a preliminary approach for the automatic generation of semantic abstractions.<n>We evaluate the approach on multiple robot descriptions and discuss the abstractions.
  arXiv  Detail & Related papers  (2026-06-10T08:08:37Z)
• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments [41.80879866951797]
  Hi-Dyna Graph is a hierarchical dynamic scene graph architecture that integrates persistent global layouts with localized dynamic semantics for embodied robotic autonomy.<n>An agent powered by large language models (LLMs) is employed to interpret the unified graph, infer latent task triggers, and generate executable instructions grounded in robotic affordances.
  arXiv  Detail & Related papers  (2025-05-30T03:35:29Z)
• FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks [52.47895046206854]
  FieldWorkArena is a benchmark for agentic AI targeting real-world field work.<n>This paper defines a new action space that agentic AI should possess for real world work environment benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T08:21:46Z)
• RoBridge: A Hierarchical Architecture Bridging Cognition and Execution for General Robotic Manipulation [90.81956345363355]
  RoBridge is a hierarchical intelligent architecture for general robotic manipulation.<n>It consists of a high-level cognitive planner (HCP) based on a large-scale pre-trained vision-language model (VLM)<n>It unleashes the procedural skill of reinforcement learning, effectively bridging the gap between cognition and execution.
  arXiv  Detail & Related papers  (2025-05-03T06:17:18Z)
• Time is on my sight: scene graph filtering for dynamic environment perception in an LLM-driven robot [0.8515309662618664]
  This paper presents a robot control architecture that addresses key challenges in human-robot interaction. The architecture uses Large Language Models to integrate diverse information sources, including natural language commands. The architecture enhances adaptability, task efficiency, and human-robot collaboration in dynamic environments.
  arXiv  Detail & Related papers  (2024-11-22T15:58:26Z)
• Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation [69.01029651113386]
  Embodied-RAG is a framework that enhances the model of an embodied agent with a non-parametric memory system.<n>At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail.<n>We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries.
  arXiv  Detail & Related papers  (2024-09-26T21:44:11Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Enhancing Graph Representation of the Environment through Local and Cloud Computation [2.9465623430708905]
  We propose a graph-based representation that provides a semantic representation of robot environments from multiple sources. To acquire information from the environment, the framework combines classical computer vision tools with modern computer vision cloud services. The proposed approach allows us to handle also small objects and integrate them into the semantic representation of the environment.
  arXiv  Detail & Related papers  (2023-09-22T08:05:32Z)
• HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing [1.3678064890824186]
  The Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates.
  arXiv  Detail & Related papers  (2021-10-20T00:41:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.