A Two-Stage Biologically Inspired Robot Navigation Framework via Reward-Modulated STDP and Obstacle-State Encoding

Abstract

This article presents a two-stage, biologically inspired neuromorphic framework for autonomous robot navigation in cluttered environments. The proposed architecture combines unsupervised spiking neural network (SNN)-based sensory abstraction with reward-modulated spike-timing-dependent plasticity (R-STDP) for decision-making. In the first stage, raw 360° LiDAR measurements are transformed into a compact, interpretable obstacle state through a lateral-inhibition-driven STDP network, yielding a low-dimensional, behaviorally relevant perception of the environment. In the second stage, navigation actions are learned via reward-modulated STDP operating on this abstracted state, supporting long-horizon goal-directed behavior without backpropagation or deep reinforcement learning. Two navigation paradigms are investigated: conventional goal-oriented navigation using relative bearing information, and a probabilistic field–based formulation that enables source-seeking behavior under goal uncertainty. The proposed approach is evaluated extensively in Gazebo and NVIDIA Isaac Sim using a TurtleBot3 platform across static, dynamic, and near-realistic environments. Experimental results demonstrate reliable navigation performance, competitive success and collision rates compared to state-of-the-art SNN and hybrid SNN–RL methods, and substantially lower estimated energy consumption. These findings highlight the effectiveness of modular, biologically plausible neuromorphic architectures for energy-efficient autonomous navigation in complex environments.