# Computational Tactile Flow for Anthropomorphic Grippers

## TL;DR

This paper introduces a computational tactile flow method inspired by human tactile perception, enabling robots to analyze tactile feedback in isolation to extract detailed surface information and improve manipulation tasks.

## Contribution

It presents a novel computational tactile flow model that maps pressure sensor data to a virtual surface and computes optical flow to analyze tactile feedback.

## Key findings

- Reveals surface motion direction and 3D structure
- Provides feedback on robot actions
- Enhances tactile perception in robotic grasping

## Abstract

Grasping objects requires tight integration between visual and tactile feedback. However, there is an inherent difference in the scale at which both these input modalities operate. It is thus necessary to be able to analyze tactile feedback in isolation in order to gain information about the surface the end-effector is operating on, such that more fine-grained features may be extracted from the surroundings. For tactile perception of the robot, inspired by the concept of the tactile flow in humans, we present the computational tactile flow to improve the analysis of the tactile feedback in robots using a Shadow Dexterous Hand. In the computational tactile flow model, given a sequence of pressure values from the tactile sensors, we define a virtual surface for the pressure values and define the tactile flow as the optical flow of this surface. We provide case studies that demonstrate how the computational tactile flow maps reveal information on the direction of motion and 3D structure of the surface, and feedback regarding the action being performed by the robot.