MemClaw-RAG: Memory-Driven Navigation and Adaptive Locomotion for Wheeled-Legged Robots in Dynamic Environments

Abstract

Object-Goal Navigation in dynamic environments remains challenging because many existing approaches rely primarily on reactive mapping and lack the ability to retain historical experience or establish structured memory associations. To address this limitation, we introduce MemClaw-RAG, an embodied multimodal framework. MemClaw-RAG includes three main components: (1) a Memory Graph Retrieval (MGR) module that leverages multimodal knowledge graphs to support semantic association and target retrieval; (2) a SelfClaw cognitive module that manages skill scheduling and task execution through memory-aware reasoning; and (3) a Hybrid Adaptive Locomotion Policy (HALP) based on deep reinforcement learning that enables efficient locomotion for wheeled-legged robots across different terrain conditions.On Habitat benchmarks, MemClaw-RAG achieves a Success Rate (SR) of 0.81 and a Success-weighted Path Length (SPL) of 0.51 on the Gibson and HM3D datasets. In the more challenging multi-layer environments of MP3D, the proposed method achieves an SR of 0.76 and an SPL of 0.48, outperforming several representative memory-based and end-to-end navigation approaches. Real-world deployment on a Unitree wheeled-legged robot further demonstrates the practicality of the system, achieving an average per-step inference latency of 55 ms on a Jetson Orin platform while maintaining stable navigation behavior in dynamic indoor environments.