Adaptive Robotic Grippers for Intelligent Manipulation: A Review of Structural Compliance, Sensing, and Control

The increasing demand for flexible, high-precision, and low-damage manufacturing is driving industrial robotic grippers toward adaptive systems with compliant interaction, force regulation, and intelligent decision-making capabilities. However, their industrial deployment remains limited by three major challenges: the trade-off between structural compliance and load-bearing capability, the insufficient coordination between sensing performance and closed-loop force control, and the limited generalization and safety validation of learning-based methods. This review presents an engineering-oriented analysis of adaptive robotic grippers based on three paradigms: structural compliance, active compliance, and learning-enabled grasping. The design principles, performance characteristics, and application limitations of compliant mechanisms, variable stiffness structures, carbon fiber composites, embedded sensing, force control, data-driven methods, reinforcement learning, and model–data fusion are discussed. A comparative framework is established according to the adaptation mechanisms, key parameters, performance boundaries, and industrial applications. The analysis shows that representative underactuated grippers typically achieve 3–19 degrees of freedom, grasping success rates of 90–98%, and load capacities of approximately 10–50 N, but remain limited in force regulation. Actively compliant grippers achieve force errors of 0.1–1 N, position errors of 0.05–0.5 mm, and control frequencies of 500 Hz–1 kHz, while learning-enabled methods require 102–104 samples for compensation and 105–106 interactions for reinforcement learning. The model–data fusion provides a practical balance between adaptability, interpretability, and safety. Future adaptive grippers will evolve toward integrated structure, sensing, learning, and control architectures with enhanced reliability and industrial applicability.