Core Viewpoint - The integration of robotics and artificial intelligence in biopharmaceutical laboratories is crucial for enhancing drug development efficiency and addressing public health crises. The article emphasizes the importance of advanced robotics in automating complex biochemical processes while highlighting existing limitations in environmental perception and adaptability [1][2]. Group 1: Overview of Biochemical Laboratories - The development of biochemical laboratories has evolved through four stages: manual operation, early automation, comprehensive automation, and intelligent automation. Each stage has progressively improved operational efficiency and reduced human error [5][10]. - Current laboratories have begun to achieve a preliminary integration of automation and intelligence, but single-robot operations still face limitations in meeting diverse experimental needs [10]. Group 2: Key Technologies in Intelligent Biochemical Laboratories - Intelligent biochemistry laboratories leverage the deep integration of AI and robotics to enhance experimental processes, significantly improving efficiency and reducing human error [11]. - Environmental perception technology is critical for robots to accurately identify and understand dynamic changes in experimental settings, ensuring safe and precise operations [11][12]. - The combination of visual perception and motion planning technologies allows robots to efficiently execute tasks in complex environments, enhancing their adaptability and robustness [12][13]. Group 3: Environmental Perception Technologies - Environmental perception in intelligent biochemical laboratories involves recognizing and locating experimental tools and reagents, understanding object characteristics, and assessing spatial relationships [14][15]. - The perception technology is categorized into three levels: target recognition, object relationship recognition, and area relationship recognition, each contributing to the robot's operational capabilities [15][16]. - Single-modal data perception methods, such as visual and LiDAR sensors, are essential for accurate pose estimation of objects in the laboratory [18][22]. Group 4: Multi-Robot Task and Motion Planning Technologies - Multi-robot collaboration is essential for complex tasks in drug development, involving task decomposition, coalition formation, task allocation, and execution [45]. - Task planning methods include predefined task planning, reinforcement learning-based planning, and large language model-based planning, each offering unique advantages for dynamic environments [46][47]. - Motion planning techniques encompass traditional path planning algorithms, imitation learning, and embodied intelligence approaches, addressing various challenges in static and dynamic environments [57].