EDEN: 内嗅皮質に着想を得た自己中心ナビゲーションのロボット実装
EDEN: Entorhinal Driven Egocentric Navigation Toward Robotic Deployment
哺乳類の内嗅皮質-海馬系に着想を得て、グリッドセル表現と強化学習を組み合わせ、視覚・運動センサのみで経路統合とゴール指向ナビゲーションを行うフレームワークEDENを提案し、シミュレータで高成功率を達成した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Mikolaj Walczak, Romina Aalishah, Wyatt Mackey, Brittany Story, David L. Boothe, Nicholas Waytowich, Xiaomin Lin, Tinoosh Mohsenin
分類: cs.RO, cs.AI
原文アブストラクト
Deep reinforcement learning agents are often fragile while humans remain adaptive and flexible to varying scenarios. To bridge this gap, we present EDEN, a biologically inspired navigation framework that integrates learned entorhinal-like grid cell representations and reinforcement learning to enable autonomous navigation. Inspired by the mammalian entorhinal-hippocampal system, EDEN allows agents to perform path integration and vector-based navigation using visual and motion sensor data. At the core of EDEN is a grid cell encoder that transforms egocentric motion into periodic spatial codes, producing low-dimensional, interpretable embeddings of position. To generate these activations from raw sensory input, we combine fiducial marker detections in the lightweight MiniWorld simulator and DINO-based visual features in the high-fidelity Gazebo simulator. These spatial representations serve as input to a policy trained with Proximal Policy Optimization (PPO), enabling dynamic, goal-directed navigation. We evaluate EDEN in both MiniWorld, for rapid prototyping, and Gazebo, which offers realistic physics and perception noise. Compared to baseline agents using raw state inputs (e.g., position, velocity) or standard convolutional image encoders, EDEN achieves a 99% success rate, within the simple scenarios, and >94% within complex floorplans with occluded paths with more efficient and reliable step-wise navigation. In addition, as a replacement of ground truth activations, we present a trainable Grid Cell encoder enabling the development of periodic grid-like patterns from vision and motion sensor data, emulating the development of such patterns within biological mammals. This work represents a step toward biologically grounded spatial intelligence in robotics, bridging neural navigation principles with reinforcement learning for scalable deployment.
