Tactile Sensing for Robotics

Why Tactile Sensing Matters

Vision alone cannot tell a robot how hard it is gripping an egg, whether a tool is about to slip, or how a deformable object is deforming under contact. Tactile sensors close that gap by providing spatially resolved force and contact information directly at the point of interaction. This data improves grasp success rates, enables contact-rich manipulation tasks, and provides a critical signal channel for imitation learning and reinforcement learning policies.

Sensor Technologies

Vision-Based Tactile Sensors

Cameras behind a soft gel membrane capture deformation patterns caused by contact. Examples include GelSight and DIGIT. They provide high-resolution contact geometry but are bulkier and require image processing pipelines.

Resistive / Capacitive Arrays

Thin-film sensor arrays measure pressure distribution across a surface. They are compact and fast but typically lower resolution than vision-based alternatives. Well-suited for detecting slip and measuring grip force.

Barometric / Pneumatic Sensors

Air-pressure-based sensors in soft fingertips detect contact force and object shape. They are simple, robust, and inexpensive—a good fit for low-cost research platforms.

Products at SVRC

Paxini Gen 3

The Paxini Gen 3 is a high-resolution tactile sensing platform with a dense sensor array and a ROS 2 driver stack. It is designed to mount on standard robot end-effectors and provides calibrated force readings at up to 100 Hz. Ideal for contact-rich manipulation research and data collection.

View Paxini Gen 3 specs and pricing →

RC G1 Glove

The RC G1 Glove is a wearable teleoperation glove with embedded tactile sensors across the fingertips and palm. It captures the demonstrator's hand pose and contact forces simultaneously, enabling high-fidelity demonstration recording for dexterous manipulation policies.

Browse teleoperation products in the store →

Integration Best Practices

• Calibrate before every session — Tactile sensor readings drift with temperature and wear. A quick zero-force calibration keeps data consistent.
• Time-synchronize with vision — Tactile and camera streams must share a common clock for multi-modal learning. Use hardware triggers or software NTP alignment.
• Record raw and processed data — Store both the raw sensor matrix and derived features (contact centroid, total force) in your dataset for maximum downstream flexibility.
• Pair with dexterous hands — Tactile sensing is most impactful when combined with multi-fingered end-effectors. See our dexterous hands guide for options.

Applications

• Slip detection and reactive grasping
• Contact-rich manipulation (insertion, pivoting, peg-in-hole)
• Deformable object handling (fabric, cables, food)
• Demonstration recording for imitation learning
• Quality inspection via surface texture classification

Next Steps

Explore the Paxini Gen 3 hardware page for detailed specs, or visit the SVRC Store to see all tactile sensing products. Questions? Reach us at contact@roboticscenter.ai.