The article discusses the distinction between Artificial Intelligence (AI) and Artificial Cognition (ACo) in the context of next-generation autonomous robotic agents. It argues that while AI is a broad term encompassing various technologies, ACo is a brain-inspired, embodied approach that emphasizes proactive knowledge acquisition through bidirectional human-robot interaction. ACo aims to avoid the mind-body dualism of traditional AI and instead integrates Bodyware and Cogniware for more effective and explainable robotic agents. The paper highlights the limitations of current AI, such as the black-box nature of neural networks and the lack of explainability, which hinder trust and ethical considerations in applications like medical diagnosis and autonomous driving. It also emphasizes the importance of cognitive penetrability, which allows humans to communicate and interact based on shared values, as a key factor in the development of autonomous robotic agents. The article suggests that ACo should be based on developmental robotics, which is a brain-inspired approach that considers the dynamic interaction between brain, body, and environment. It discusses the principles of developmental robotics, including the role of embodiment, personal/social knowledge, and the importance of prospection in cognitive processes. The paper also highlights the need for a unified framework that integrates various research threads, such as the principles of developmental robotics, action representation with prospection capabilities, and the crucial role of social interaction. The article concludes that ACo offers a more promising approach for the development of autonomous robotic agents that can better understand and interact with their environment, leading to more effective and trustworthy systems.The article discusses the distinction between Artificial Intelligence (AI) and Artificial Cognition (ACo) in the context of next-generation autonomous robotic agents. It argues that while AI is a broad term encompassing various technologies, ACo is a brain-inspired, embodied approach that emphasizes proactive knowledge acquisition through bidirectional human-robot interaction. ACo aims to avoid the mind-body dualism of traditional AI and instead integrates Bodyware and Cogniware for more effective and explainable robotic agents. The paper highlights the limitations of current AI, such as the black-box nature of neural networks and the lack of explainability, which hinder trust and ethical considerations in applications like medical diagnosis and autonomous driving. It also emphasizes the importance of cognitive penetrability, which allows humans to communicate and interact based on shared values, as a key factor in the development of autonomous robotic agents. The article suggests that ACo should be based on developmental robotics, which is a brain-inspired approach that considers the dynamic interaction between brain, body, and environment. It discusses the principles of developmental robotics, including the role of embodiment, personal/social knowledge, and the importance of prospection in cognitive processes. The paper also highlights the need for a unified framework that integrates various research threads, such as the principles of developmental robotics, action representation with prospection capabilities, and the crucial role of social interaction. The article concludes that ACo offers a more promising approach for the development of autonomous robotic agents that can better understand and interact with their environment, leading to more effective and trustworthy systems.