Visuotactile Affordances for Cloth Manipulation with Local Control

Neha Sunil, Shaoxiong Wang, Yu She, Edward Adelson, Alberto Rodriguez Garcia

Keywords: Multi-modal learning, Cloth manipulation, Tactile control

TL;DR: Visuotactile edge grasp affordances and tactile sliding for cloth manipulation

Abstract: Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. Doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. We develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time.

Student First Author: yes

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2212.05108/code)