DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation

June 1, 2020


Despite decades of research, general purpose inhand manipulation remains one of the unsolved challenges of robotics. One of the contributing factors that limit current robotic manipulation systems is the difficulty of precisely sensing contact forces – sensing and reasoning about contact forces are crucial to accurately control interactions with the environment. As a step towards enabling better robotic manipulation, we introduce DIGIT, an inexpensive, compact, and high-resolution tactile sensor geared towards in-hand manipulation. DIGIT improves upon past vision-based tactile sensors by miniaturizing the form factor to be mountable on multi-fingered hands, and by providing several design improvements that result in an easier, more repeatable manufacturing process, and enhanced reliability. We demonstrate the capabilities of the DIGIT sensor by training deep neural network model-based controllers to manipulate glass marbles in-hand with a multi-finger robotic hand. To provide the robotic community access to reliable and low-cost tactile sensors, we open-source the DIGIT design at

Download the Paper


Written by

Mike Lambeta

Po-Wei Chou

Stephen Tian

Brian Yang

Benjamin Maloon

Victoria Rose Most

Dave Stroud

Raymond Santos

Ahmad Byagowi

Gregg Kammerer

Dinesh Jayaraman

Roberto Calandra


IEEE Robotics and Automation Letters (RA-L)

Related Publications

November 27, 2022

Core Machine Learning

Neural Attentive Circuits

Nicolas Ballas, Bernhard Schölkopf, Chris Pal, Francesco Locatello, Li Erran, Martin Weiss, Nasim Rahaman, Yoshua Bengio

November 27, 2022

November 27, 2022

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann

November 27, 2022

November 16, 2022


Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Kushal Tirumala, Aram H. Markosyan, Armen Aghajanyan, Luke Zettlemoyer

November 16, 2022

November 10, 2022

Computer Vision

Learning State-Aware Visual Representations from Audible Interactions

Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado

November 10, 2022

April 08, 2021

Responsible AI


Towards measuring fairness in AI: the Casual Conversations dataset

Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer

April 08, 2021

April 30, 2018

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings | Facebook AI Research

Tomer Galanti, Lior Wolf, Sagie Benaim

April 30, 2018

April 30, 2018

Computer Vision

NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

December 11, 2019

Speech & Audio

Computer Vision

Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research

Eliya Nachmani, Lior Wolf

December 11, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.