具身智能体主动迎战对抗攻击，清华团队提出主动防御框架

Core Insights - The article discusses the introduction of the REIN-EAD framework, which enables embodied intelligent agents to actively defend against adversarial attacks by enhancing their perception and decision-making capabilities [1][3][8]. Group 1: Adversarial Attacks and Current Defenses - Adversarial attacks pose significant threats to the safety and reliability of visual perception systems, particularly in critical areas like facial recognition and autonomous driving [2]. - Existing defense methods primarily rely on passive strategies, such as adversarial training and input purification, which may fail against unknown or adaptive attacks [2][3]. Group 2: REIN-EAD Framework - The REIN-EAD framework integrates perception and strategy modules to simulate human-like motion vision mechanisms, allowing continuous observation and exploration in dynamic environments [3][8]. - It employs a cumulative information exploration reinforcement learning method to optimize active strategies, enhancing the system's ability to identify and adapt to potential threats [4][11]. Group 3: Offline Adversarial Patch Approximation (OAPA) - The OAPA technique addresses the computational challenges of adversarial training in 3D environments by creating a universal defense mechanism that does not rely on opponent information [5][6][18]. - This method significantly reduces training costs while maintaining robust defense capabilities against unknown or adaptive attacks [6][18]. Group 4: Performance and Generalization - REIN-EAD demonstrates superior performance across multiple tasks and environments, outperforming existing passive defense methods in resisting various unknown and adaptive attacks [7][19]. - The framework's strong generalization ability and adaptability to complex real-world scenarios highlight its potential applications in safety-critical systems [7][19][31].