Memorize My Movement: Efficient Sensorimotor Navigation With Self-Motion-Based Spatial Cognition

Abstract

Navigation is a fundamental capability for robots to operate in expansive spaces. Reliable navigation in unknown environments is crucial for deploying robots in areas such as disaster rescue and industrial inspection. In such scenarios, it is essential for robots to construct memories based on historical data to support long-term, optimized decision-making. However, many existing techniques focus on memorizing direct features from raw perceptions, often resulting in redundancy due to irrelevant textures and areas. This approach leads to inefficiencies in computation and storage, and produces a memory structure that lacks general applicability. We suggest that it may not be necessary to store specific scene features. Instead, recalling the robot’s episodic movements could provide sufficient cognitive cues for navigation. To address this, we introduce Memory Enhanced Navigation with Embedded Odometry (MENEO), a framework consisting of three steps: ego-motion estimation, memory aggregation, and adaptive policy generation. MENEO offers two main advantages: its streamlined architecture significantly boosts computational and storage efficiency, and its universal design supports various sensor modalities, adapts to multiple navigation tasks, and accommodates different scenarios. We test MENEO in two different environments: maze exploration using a lidar-IMU sensor, and image-goal visual navigation in photorealistic indoor scenes. In both cases, MENEO demonstrates competitive navigation performance, outperforming existing methods by reducing storage and computational requirements. Additionally, MENEO’s compact memory representation not only enhances adaptability across diverse environments but also shows flexibility in real-world applications. Note to Practitioners—In learning-based navigation, the memory mechanism is essential for long-term optimized policies. It allows intelligent robots to make informed decisions by utilizing a wide range of temporal and spatial cues derived from historical data. Traditional methods use various types of memory (such as internal, unstructured, or structured), but these often result in computational and storage inefficiencies due to the direct inclusion of complex and redundant raw scene features. Furthermore, because these memory systems are closely linked to specific scene features, they lack general applicability across different sensor configurations, scene types, and tasks. In this paper, we aim to eliminate the need to directly manage the redundant environmental features found in previous memory structures. Instead, we propose focusing solely on memorizing a robot’s self-movements. Since the pose sequence is streamlined and compact, our approach not only enhances computational and storage efficiency but also improves the interpretability and universality of the navigation system. This advantage enables MENEO to be seamlessly integrated into a wide variety of intelligent navigation systems. It is especially beneficial for small robots with limited computing power, such as those used in search and rescue operations, where enhanced memory can greatly enhance their autonomous navigation capabilities.