Soft tactile sensing tracks contact forces and task frames in real time
Tactile Reconstruction of Contact Task Frames and Forces for Hybrid Force/Motion Control
Robotics
Summary
Robots need to know how hard they're pressing and in which directions to move or apply force. This work helps robots estimate these forces and directions just by feeling, using soft optical touch sensors. The authors taught a robot to recognize touch patterns without relying on traditional force sensors or detailed environment models. Their system updates these contact estimates continuously, enabling smoother and safer interactions controlled by touch alone.
What this means in practice
- •For industrial robot programmers: Use tactile images alone to estimate contact forces and orientations in real time for flexible control of robot interactions without external force sensors.
- •For robotic rehabilitation device developers: Enable devices to regulate touch and force precisely via soft tactile sensing without complex force-torque sensors, improving patient comfort and safety.
Authors
Antonio Rapuano, Simone Orelli, Barbara Bazzana, Antonio Franchi, Alessandro De Luca
Abstract
Hybrid force/motion control requires knowledge of the interaction force and of a task frame defining the force- and motion-controlled directions. These quantities are usually obtained from force/torque sensing or model-based residuals, often assuming also a nominal environment model. This work addresses the online estimation of the contact force and a possibly time-varying task frame using only soft optical tactile sensing, under the assumption of locally planar contact with a negligible contact moment. The proposed method maps a single image of the deformed elastomer of a soft optical tactile sensor to observable contact variables: indentation depth, two surface-to-sensor tilt angles, and 3D contact force, each with a per-sample uncertainty estimate. The mapping is learned through a self-labeling acquisition procedure, in which a manipulator imposes controlled contacts while an auxiliary Force/Torque sensor is used offline to provide ground-truth labels. The tactile measurement is then fused with robot proprioceptive data in an Extended Kalman Filter, producing a continuously updated estimate of the contact task frame and of the interaction force. Control experiments with a DigiTac sensor mounted on a UR10 manipulator demonstrate closed-loop contact force regulation against a flat rigid board in linear and angular motion by a human operator, with touch as the only exteroceptive feedback.