###### Abstract
Meanwhile, reinforcement learning (RL) and inverse reinforcement learning (IRL) have gained traction in robotic path planning. [PERSON] [20] compared Q-Learning and Sarsa algorithms, highlighting parameter optimization for safety enhancement in dynamic environments; [PERSON] et al. [21] proposed a globally guided RL framework (G2 RL) achieving near-centralized planning efficiency in distributed multi-robot scenarios; and [PERSON] [22] improved trajectory smoothness by integrating path graph preprocessing with Q-Learning. Nevertheless, traditional methods depend on prior environmental knowledge, struggling to adapt to unknown dynamic risks (e.g., random obstacles or terrain variations). This study introduces an Embodied Intelligence Reinforcement Learning (EIRL) framework that synthesizes safety-conscious global path strategies through computational modeling of human navigation preferences. The methodology initiates with neurocognitive signal translation, where electroencephalogram (EEG) patterns are transformed into robotic control commands using a lightweight classification architecture, generating expert-level navigation trajectories. Building upon these empirical demonstrations, the framework subsequently employs apprenticeship learning to distill essential feature expectations and quantify reward weight distributions inherent in human decision-making processes. These optimized reward functions are then systematically integrated into Q-Learning iterations,enabling dynamic path planning that intrinsically balances exploration efficiency with collision avoidance. Experimental validation in the Frozen Lake V1 benchmark environment reveals the framework's superior safety performance, achieving over 50% reduction in path risk metrics compared with conventional reinforcement learning and \(A^{*}\) search algorithms. This neurocognitive-inspired approach establishes a novel paradigm for developing human-aligned autonomous systems that preserve biological decision-making advantages while addressing the safety-critical requirements of real-world robotic operations. The main contributions of this study include:  
1. Providing a physical-operation-free robot control interface for individuals with motor impairments, enhancing human-robot collaboration inclusivity;
2. Learning reward functions from EEG-implicit decision preferences via IRL, reducing reliance on prior maps;
3. Establishing a synergistic \"brain-machine-environment\" learning framework by integrating neural feedback with path execution results. The research progression is structured to systematically bridge critical gaps in EEG-driven path planning. Initial investigations revealed two persistent limitations: conventional reinforcement learning frameworks struggle to decode human-like risk sensitivity from sparse neural signals, while existing EEG classification methods prioritize accuracy over real-time deployability. To address these challenges, our methodology integrates lightweight neural decoding with inverse reward modeling, enabling policy optimization that inherently balances safety and efficiency. This phased approach ensures theoretical rigor while maintaining practicality for real-world robotic applications. Conventional navigation systems rely on explicit environmental models or reactive RL policies, both of which struggle in partially observable, safety-critical scenarios (e.g., disaster rescue). EIRL addresses this gap by integrating EEG as a safety prior--a paradigm shift that enables proactive risk mitigation through human neurocognitive patterns. While traditional baselines (RL/\(A^{*}\)) are used for benchmarking, their inability to leverage neural data inherently limits comparability, as no prior work combines EEG with IRL for navigation.