GRIP让机器人在复杂环境中更智能地规划路径，支持真实世界部署。
GRIP: A Unified Framework for Grid-Based Relay and Co-Occurrence-Aware Planning in Dynamic Environments
- 基于网格的中间规划框架，融合语义感知与符号推理。
- 长任务成功率提升9.6%，路径效率超2倍，优于现有方法。
- 适合需要可解释性与真实环境适应性的机器人导航研究者。
机器人在动态、杂乱且语义复杂的环境中导航时，需融合感知、符号推理与空间规划以实现跨布局和物体类别的泛化。现有方法常依赖静态先验或有限记忆，限制了在部分可观测与语义模糊下的适应能力。我们提出GRIP（Grid-based Relay with Intermediate Planning），一种统一、模块化的框架，包含三种可扩展变体：GRIP-L（轻量级），通过语义占据网格优化符号导航；GRIP-F（全功能），支持多跳锚点链式连接与大模型内省；GRIP-R（真实世界），可在感知不确定性下实现物理机器人部署。GRIP集成动态2D网格构建、开放词汇物体定位、共现感知符号规划及混合策略执行（行为克隆、D*搜索、网格条件控制）。在AI2-THOR和RoboTHOR基准上的实证结果表明，GRIP在长程任务中成功率最高提升9.6%，路径效率（SPL和SAE）提升超过2倍。定性分析显示其在模糊场景中生成可解释的符号规划。在Jetbot上的真实世界部署进一步验证了其在传感器噪声与环境变化下的泛化能力。这些结果使GRIP成为连接仿真与真实导航的鲁棒、可扩展且可解释的框架。
原文摘要 · Abstract (English)
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.
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