Human-Inspired Grasping State Regulation Strategy Based on Visual Feedforward and Tactile Gating Reflexes
In unstructured environments, endowing robots with the ability to dexterously and safely grasp unknown objects presents a critical challenge. Existing control methods struggle to adapt dynamically like human hands, failing to balance grasping stability and object safety. Inspired by human grasping mechanisms, we propose a grasping state regulation strategy based on visual feedforward and tactile gating reflexes to dynamically adjust grasping force. First, guided by the idea that visual information can provide object-dependent expectations before contact, we developed a Two-Stage Mass Estimation Framework and a Vision-Based Friction Coefficient Estimation Framework. They extract the object’s mass and friction coefficients as physical priors from visual inputs, providing reliable initial expectations for subsequent grasping force regulation. Next, the Multimodal State Classifier compares the expected tactile-state representation derived from physical priors and global visual information with the actual tactile-state representation extracted from real-time tactile feedback, and outputs discrete corrective actions. To evaluate performance, we introduce a new metric called the anthropomorphic rate. It quantifies the similarity between the robot-applied force and the human instinctive grasping force. We verified our framework through comprehensive offline and online experiments. Results demonstrate that our system achieves a 91.82% grasp success rate and a 92.65% anthropomorphic rate. These results demonstrate the effectiveness of the proposed strategy in real-world physical interactions.