GRIP: A Unified Framework for Grid-Based Relay and Co-Occurrence-Aware Planning in Dynamic Environments

• URL: http://arxiv.org/abs/2510.10865v1
• Date: Mon, 13 Oct 2025 00:13:37 GMT
• Title: GRIP: A Unified Framework for Grid-Based Relay and Co-Occurrence-Aware Planning in Dynamic Environments
• Abstract summary: We present GRIP, Grid-based Relay with Intermediate Planning, a unified, modular framework with three scalable variants.<n>GRIP integrates dynamic 2D grid construction, open-vocabulary object grounding, co-occurrence-aware symbolic planning, and hybrid policy execution.<n> Empirical results on AI2-THOR and RoboTHOR benchmarks show that GRIP achieves up to 9.6% higher success rates and over $2times$ improvement in path efficiency.
• Score: 1.3543952438735405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots navigating dynamic, cluttered, and semantically complex environments must integrate perception, symbolic reasoning, and spatial planning to generalize across diverse layouts and object categories. Existing methods often rely on static priors or limited memory, constraining adaptability under partial observability and semantic ambiguity. We present GRIP, Grid-based Relay with Intermediate Planning, a unified, modular framework with three scalable variants: GRIP-L (Lightweight), optimized for symbolic navigation via semantic occupancy grids; GRIP-F (Full), supporting multi-hop anchor chaining and LLM-based introspection; and GRIP-R (Real-World), enabling physical robot deployment under perceptual uncertainty. GRIP integrates dynamic 2D grid construction, open-vocabulary object grounding, co-occurrence-aware symbolic planning, and hybrid policy execution using behavioral cloning, D* search, and grid-conditioned control. Empirical results on AI2-THOR and RoboTHOR benchmarks show that GRIP achieves up to 9.6% higher success rates and over $2\times$ improvement in path efficiency (SPL and SAE) on long-horizon tasks. Qualitative analyses reveal interpretable symbolic plans in ambiguous scenes. Real-world deployment on a Jetbot further validates GRIP's generalization under sensor noise and environmental variation. These results position GRIP as a robust, scalable, and explainable framework bridging simulation and real-world navigation.

Related papers

• Hierarchical 3D Scene Graph Construction and Belief-based Planning for Semantic Navigation [34.08516675160383]
  We propose a zero-shot semantic navigation framework for embodied agents.<n>Our method incrementally maintains an online Hierarchical 3D Scene Graph (HSG) over objects, zones, and regions, serving as a compact state abstraction for global planning.<n>Building on this memory, we introduce a hierarchical belief-based planning framework that fuses semantic priors with exploration evidence on the HSG, and performs finite-horizon rollouts on an HSG-based simulator to explicitly estimate the long-term expected returns of candidate macro-actions.
  arXiv  Detail & Related papers  (2026-06-30T03:01:44Z)
• SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation [26.212246536363363]
  Object-Goal Navigation (Nav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations.<n>We propose SAGE-Nav, a novel hierarchical framework that integrates the reasoning capabilities of Large Language Models (LLMs) with dynamic scene graphs.<n>We show that SAGE-Nav achieves substantial gains in navigation efficiency and zero-shot generalization while maintaining the low control latency required for physical robotic deployment.
  arXiv  Detail & Related papers  (2026-06-24T07:24:39Z)
• RAGNav: A Retrieval-Augmented Topological Reasoning Framework for Multi-Goal Visual-Language Navigation [1.7508558850131373]
  Vision-Language Navigation (VLN) is evolving from single-point pathfinding toward the more challenging Multi-Goal VLN.<n>RAGNav is a framework that bridges the gap between semantic reasoning and physical structure.
  arXiv  Detail & Related papers  (2026-03-04T05:31:33Z)
• HeRO: Hierarchical 3D Semantic Representation for Pose-aware Object Manipulation [54.325346533275074]
  HeRO is a diffusion-based policy that couples geometry and semantics via hierarchical semantic fields.<n>In various tests, HeRO establishes a new state-of-the-art, improving success on Place Dual Shoes by 12.3% and averaging 6.5% gains across six challenging pose-aware tasks.
  arXiv  Detail & Related papers  (2026-02-21T12:29:10Z)
• Dynamic Topology Awareness: Breaking the Granularity Rigidity in Vision-Language Navigation [22.876516699004814]
  Vision-Language Navigation in Continuous Environments (VLN-CE) presents a core challenge: grounding high-level linguistic instructions into precise, safe, and long-horizon spatial actions.<n>Explicit topological maps have proven to be a vital solution for providing robust spatial memory in such tasks.<n>Existing topological planning methods suffer from a "Granularity Rigidity" problem.<n>We propose DGNav, a framework for Dynamic Topological Navigation, introducing a context-aware mechanism to modulate map density and connectivity on-the-fly.
  arXiv  Detail & Related papers  (2026-01-29T14:06:23Z)
• Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration [11.655381195889428]
  We propose Deep GraphRAG, a framework designed for a balanced approach to hierarchical retrieval and adaptive integration.<n>It introduces a hierarchical global-to-local retrieval strategy that integrates macroscopic inter-community and microscopic intra-community contextual relations.<n>A beam search-optimized dynamic re-ranking module guides this process, continuously filtering candidates to balance efficiency and global comprehensiveness.
  arXiv  Detail & Related papers  (2026-01-16T10:02:31Z)
• Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation [0.0]
  Hybrid Motion Planning with Deep Reinforcement Learning (HMP-DRL)<n>We propose a graph-based global planner to generate a path, which is integrated into a local DRL policy via a sequence of checkpoints encoded in both the state space and reward function.<n>To ensure social compliance, the local planner employs an entity-aware reward structure that dynamically adjusts safety margins and penalties based on the semantic type of surrounding agents.
  arXiv  Detail & Related papers  (2025-12-31T05:58:57Z)
• SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images [49.52402091341301]
  Current models can parse simple, single-target commands but fail when presented with complex geospatial scenarios.<n>We present LaSeRS, the first large-scale dataset built for comprehensive training and evaluation.<n>We also propose SegEarth-R2, an MLLM architecture designed for comprehensive language-guided segmentation in RS.
  arXiv  Detail & Related papers  (2025-12-23T03:10:17Z)
• Bridging Simulation and Reality: Cross-Domain Transfer with Semantic 2D Gaussian Splatting [60.513168855890974]
  Cross-domain transfer in robotic manipulation is a longstanding challenge due to the significant domain gap between simulated and real-world environments.<n>We propose Semantic 2D Gaussian Splatting (S2GS), a novel representation method that extracts object-centric, domain-invariant spatial features.<n>S2GS significantly improves sim-to-real transferability, maintaining high and stable task performance in real-world scenarios.
  arXiv  Detail & Related papers  (2025-12-04T12:16:15Z)
• ZeroGR: A Generalizable and Scalable Framework for Zero-Shot Generative Retrieval [125.19156877994612]
  Generative retrieval (GR) reformulates information retrieval (IR) by framing it as the generation of document identifiers (docids)<n>We propose textscZeroGR, a zero-shot generative retrieval framework that leverages natural language instructions to extend GR across a wide range of IR tasks.<n>Specifically, textscZeroGR is composed of three key components: (i) an LM-based docid generator that unifies heterogeneous documents into semantically meaningful docids; (ii) an instruction-tuned query generator that generates diverse types of queries from natural language task descriptions to enhance
  arXiv  Detail & Related papers  (2025-10-12T03:04:24Z)
• Generalist Scanner Meets Specialist Locator: A Synergistic Coarse-to-Fine Framework for Robust GUI Grounding [53.14935624161711]
  GMS: Generalist Scanner Meets Specialist Locator is a synergistic coarse-to-fine framework that effectively improves GUI grounding performance.<n>This design is inspired by how humans perform GUI grounding, where the eyes scan the interface and the brain focuses on interpretation and localization.<n> Experimental results on the ScreenSpot-Pro dataset show that while the 'Scanner' and 'Locator' models achieve only $2.0%$ and $3.7%$ accuracy respectively when used independently, their integration within GMS framework yields an overall accuracy of $35.7%$.
  arXiv  Detail & Related papers  (2025-09-29T00:06:31Z)
• Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation [69.30586607892842]
  We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation.<n>GE integrates policy learning, evaluation, and simulation within a single video-generative framework.
  arXiv  Detail & Related papers  (2025-08-07T17:59:44Z)
• Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System [7.266794815157721]
  We propose a hierarchical framework integrating a prompted Large Language Model (LLM) and a fine-tuned Vision Language Model (VLM)<n>The LLM decomposes tasks and constructs a global semantic map, while the VLM extracts task-specified semantic labels and 2D spatial information from aerial images to support local planning.<n>This is the first demonstration of an aerial-ground heterogeneous system integrating VLM-based perception with LLM-driven task reasoning and motion planning.
  arXiv  Detail & Related papers  (2025-06-05T13:27:41Z)
• 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)
• OMEGA: Efficient Occlusion-Aware Navigation for Air-Ground Robot in Dynamic Environments via State Space Model [12.096387853748938]
  Air-ground robots (AGRs) are widely used in surveillance and disaster response.<n>Current AGR navigation systems perform well in static environments.<n>However, these systems face challenges in dynamic, severe occlusion scenes.<n>We propose OccMamba with an Efficient AGR-Planner to address these problems.
  arXiv  Detail & Related papers  (2024-08-20T07:50:29Z)

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.