Robotic grasping in unstructured environments demands closed-loop feedback that goes beyond conventional fixed open-loop force control. Vision sensors suffer from occlusion at the instant of contact, whereas wrist-mounted force/ torque sensors cannot resolve local shear stress distributions or incipient slip at multi-contact interfaces. Tactile sensors bridge this gap by mapping high-dimensional contact physics directly and in real time. This paper surveys grasp intelligence technology within a Perception–State Estimation–Action framework. Specifically, it reviews optical and arraytype tactile sensors; slip detection algorithms, ranging from physics-based local deformation analysis to deep spatiotemporal networks; and active closed-loop force regulation, including diffusion-policy-based reactive control. Applications in precision semiconductor and electronics assembly are then discussed. Finally, outstanding challenges in mechanical durability, ultra-low-latency data processing, intelligent tactile skin, and multi-modal Vision-Language-Action integration are identified as priorities for future research.
Although 3D shape completion facilitates robotic grasping from partial observations, deterministic completion models overlook regional uncertainty, which can result in suboptimal grasp poses. To address this limitation, we propose an uncertainty-aware robotic grasping framework that couples geometric completion confidence with physical constraints. The framework integrates a Multi-rate Dropout Ensemble (MDE) into the shape completion network, enabling it to restore 3D geometry while simultaneously quantifying epistemic uncertainty. The quantified uncertainty is then mapped onto a 3D gripper bounding box to evaluate the local reliability of each grasp candidate. Evaluations in simulation on a diverse set of unseen objects demonstrate that our approach effectively suppresses unreliable grasp hypotheses. Overall, the framework significantly reduces geometric reconstruction errors and improves grasping success rates, confirming its strong generalization capability and operational safety under occlusion.
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