A Reinforcement Learning-Based Model for Mapping and Goal-Directed Navigation Using Multiscale Place Fields

• URL: http://arxiv.org/abs/2601.03520v1
• Date: Wed, 07 Jan 2026 02:10:52 GMT
• Title: A Reinforcement Learning-Based Model for Mapping and Goal-Directed Navigation Using Multiscale Place Fields
• Authors: Bekarys Dukenbaev, Andrew Gerstenslager, Alexander Johnson, Ali A. Minai,
• Abstract summary: This paper introduces a new robust model that employs parallel layers of place fields at multiple spatial scales.<n> Simulations show that the model improves path efficiency and accelerates learning compared to single-scale baselines.
• Score: 41.331598965375186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous navigation in complex and partially observable environments remains a central challenge in robotics. Several bio-inspired models of mapping and navigation based on place cells in the mammalian hippocampus have been proposed. This paper introduces a new robust model that employs parallel layers of place fields at multiple spatial scales, a replay-based reward mechanism, and dynamic scale fusion. Simulations show that the model improves path efficiency and accelerates learning compared to single-scale baselines, highlighting the value of multiscale spatial representations for adaptive robot navigation.

Related papers

• HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving [59.55918581964678]
  HybridWorldSim is a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents.<n>We release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities.
  arXiv  Detail & Related papers  (2025-11-27T07:53:16Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• Learning Dynamic Cognitive Map with Autonomous Navigation [8.301959009586861]
  We introduce a novel computational model to navigate and map a space rooted in biologically inspired principles. Our model incorporates a dynamically expanding cognitive map over predicted poses within an Active Inference framework. Our model achieves this without prior knowledge of observation and world dimensions, underscoring its robustness and efficacy in navigating intricate environments.
  arXiv  Detail & Related papers  (2024-11-13T08:59:53Z)
• Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation [8.261491880782769]
  We propose a novel approach for learning a probabilistic, terrain- and robot-aware forward dynamics model (TRADYN) We evaluate our method in simulation for 2D navigation of a robot with uni-cycle dynamics with varying properties on terrain with spatially varying friction coefficients.
  arXiv  Detail & Related papers  (2024-09-17T16:46:39Z)
• Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning [0.0]
  We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios.
  arXiv  Detail & Related papers  (2024-08-07T14:32:41Z)
• Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron [1.1510009152620668]
  This study sets the stage for interpretable regional-scale MCP-based hydrological modeling (using large sample data) by using neural architecture search to determine appropriate minimal representations for catchments in different hydroclimatic regimes.
  arXiv  Detail & Related papers  (2024-01-25T21:26:49Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation [0.3518016233072556]
  This paper presents a synthetic dataset to train semantic segmentation networks and a collection of virtual scenarios for a fast evaluation of navigation algorithms. An automatic parametric approach is developed to explore different field geometries and features. The simulation framework and the dataset have been evaluated by training a deep segmentation network on different crops and benchmarking the resulting navigation.
  arXiv  Detail & Related papers  (2023-06-27T14:46:09Z)
• Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System [0.5522489572615558]
  We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.
  arXiv  Detail & Related papers  (2022-09-14T01:55:15Z)
• Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
  This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
  arXiv  Detail & Related papers  (2020-07-01T17:52:32Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.