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.