Multi-Modal Sensorized Soft Gripper for Reliable Grasping

Grasp uncertainty in robotic manipulation is a known issue where grippers, especially soft grippers, struggle to generate secure holds on objects. The problem arises from computational factors such as incomplete data on object properties, and is magnified by environmental factors such as occlusion or motion. It presents a critical concern, as unstable grasps may lead to dropped items, damaged goods, or safety hazards in industrial applications. This paper presents a human-inspired, sensorized soft gripper to address this issue. The design incorporates soft fingers with a multimodal vision system and real-time monitoring of finger-object interactions. Additionally, unique features are implemented through proprioception markers to build a deeper understanding of the gripper’s interaction with objects. Experiments with the YCB object set demonstrate the gripper’s robustness in dynamic manipulation, while external disturbance tests highlight the stability of various grasp configurations. A predictive framework is proposed which uses the validated features to predict grasp success. Experimental results show significant promise for safe and adaptive robotic grasping and manipulation across various industries.