Manipulation by Feel: Touch-Based Control with Deep Predictive Models

TL;DR

This paper introduces deep tactile MPC, a novel framework that enables robots to perform tactile servoing and manipulation tasks using deep neural network models trained on raw tactile sensor data, without manual supervision.

Contribution

It presents a new approach combining high-resolution tactile sensing with deep learning to enable tactile-based control and manipulation without explicit physics models or manual supervision.

Findings

Robots successfully manipulate objects using learned tactile predictive models.

Deep tactile MPC enables repositioning objects to user-defined tactile goals.

The method operates effectively with unsupervised autonomous interaction.

Abstract

Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc