<p>Soft grippers research is gaining increasing attention for their flexibility. However, the conventional soft gripper primarily focuses on soft fingers, without considering the palm. This makes grasping forces concentrated in the fingertip areas, resulting in objects being prone to damage and instability during handling, especially for delicate items. Additionally, pre-transportation classification faces challenges: tactile methods are complex, visual methods are environment-sensitive, and both struggle with similar objects. To address these problems, inspired by the human hand’s transition between finger grasp and palm support and the lotus’s hierarchical structure, this paper proposes a dual-layer gripper, named IOSGripper. It features four pneumatic soft fingers and a rotational soft-rigid palm. Through coordinated control of the fingers and palm, it transitions concentrated fingertip squeeze force to distributed palm support force, reducing squeeze force and squeeze duration. Moreover, it integrates a range sensor and four load cells, enabling stable and accurate measurements of the object’s height and weight. Furthermore, a classifier is developed based on K-nearest neighbor algorithm, allowing real-time object classification. Experiments demonstrate that compared to only using soft fingers, the IOSGripper significantly reduces the squeeze force on the objects (with 0 N squeeze force during palm support) and damage on the delicate object, while improving grasping stability. Its height and weight measurement errors are within 2&#xa0;mm and 1&#xa0;g, respectively. And it achieves high accuracy in three test scenarios, including classifying similar objects. This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.</p>