Capacitive Strain Sensor System for Soft-Rigid Hybrid Robotic Grippers

Abstract

Soft-rigid hybrid robotic grippers offer several advantages over traditional rigid grippers, including the ability to conform to diverse object geometries, exhibit human-safe operation, and operate at potentially lower cost. However, despite recent progress in robotic gripper technologies, soft-rigid hybrid systems still face significant challenges in miniaturization, robustness, speed, sensing integration, and control. This work addresses these challenges by integrating capacitive strain sensors into a soft-rigid robotic finger and developing a learning-based method for mapping frequency-dependent electrical signals to localized strain states. These sensors enable reconstruction of the gripper’s joint configuration through a single electrical interface, supporting control policies for executing more precise and dexterous manipulations across a range of object geometries and stiffness profiles. Measurements of parallel capacitance and dissipation factor versus frequency were collected at various local strain states using an LCR meter and later replicated using a capacitance-to-digital converter for real-time implementation. Using this frequency-sweep data, an artificial neural network was trained in TensorFlow to accurately infer the sensor’s strain state. After sensor integration into multi-segment robotic fingers, the system achieved 99.05% classification accuracy and a 2.02° mean absolute error (MAE) in regression. This proprioceptive sensing system enables robotic grippers to adjust their grip dynamically and autonomously. For each finger, only a single sensor and electrical interface are required to localize and quantify deformation across multiple joints along the finger unit. By eliminating the need for multiple sensing modules per joint, this architecture significantly simplifies both design and manufacturability. The proposed sensing framework has potential applications across robotics, prosthetics, assistive technologies, and wearable systems.