Building an Ecosystem for Research on Touch Sensing

Roberto Calandra

ICRA2021 ViTac Workshop - 04 June 2021

Facebook AI Research

https://slides.com/rcalandra/vitac2021

Touch Sensing is Hard

• Interdisciplinary field which requires vertical integration. From hardware design to touch processing; From robot control to applications.
• Many ad-hoc solutions and little re-use of existing components
  (i.e., We keep reinventing the wheel over and over)
• High entrance bar for new researchers and practitioners

• How can we lower the entrance bar?
• How can we improve reproducibility?
• How can we accelerate research by re-using existing components?

What do we need to make Touch Sensing practical and useful?

Making Touch Sensing Ubiquitous

DIGIT

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Examples of DIGIT Measurements

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Comparison

BioTac

DIGIT

~15,000 $

Cost

~15 $*

Resolution

29
contact points

307,200
contact points

Mounted on multi-finger hands

Open-source

https://digit.ml

1000x
Higher resolution

1000x
Cheaper

* manufacturing cost for 1000 units

Making Touch Sensing Ubiquitous

Touch Simulation

• Most rigid-body-dynamics physics engines include some form of traditional tactile sensor (i.e., low-dimensional)
• These simulators became slower with the increasing number of contact points
• For 1000+ contact points provided by vision-based tactile sensors, they became impractical

TACTO

Wang, S.; Lambeta, M.; Chou, P.-W. & Calandra, R.
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
Under Review, 2020, Online: https://arxiv.org/abs/2012.08456

Features

• Fast (>100 fps)
• General-purpose
• Physics accuracy:
  • Modeling of the contacts is delegated to the physics engine of choice (e.g., PyBullet)
  • Works with rigid body
• Rendering accuracy:
  • Very accurate
  • Simulate "soft" deformation of the elastomer with growing forces
  • Can be calibrated from real sensors (e.g., DIGIT)
• Open-source -- Anybody can contribute

Wang, S.; Lambeta, M.; Chou, P.-W. & Calandra, R.
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
Under Review, 2020, Online: https://arxiv.org/abs/2012.08456

Software Architecture

Rendering from Simulated DIGIT

Wang, S.; Lambeta, M.; Chou, P.-W. & Calandra, R.
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
Under Review, 2020, Online: https://arxiv.org/abs/2012.08456

Making Touch Sensing Ubiquitous

The Next Breakthrough will be in Touch

Creating a Science of Touch Processing

Some of the open questions:

• What are good features for touch?
• Do we need sensor standardization?
  • What representation do we want/need for touch?
  • What sensorial information do we even want/need for touch?
• What are the useful structures in computational models for touch?
• What are the useful metrics to characterize touch?
• How can we quantify the human psychophysics of touch?
• What are the different tasks that can benefit from touch?
• What are meaningful benchmarks for touch processing?

Very limited literature about computational processing of touch sensing

PyTouch: A Machine Learning Library
for Touch Processing

Goal: Create the equivalent of OpenCV for Touch

Lambeta, M.; Xu, H.; Xu, J.; Chou, P.-W.; Wang, S.; Darrell, T. & Calandra, R.
PyTouch: A Machine Learning Library for Touch Processing
IEEE International Conference on Robotics and Automation (ICRA), 2021, Online: https://arxiv.org/abs/2105.12791

Features of PyTouch

• First Machine Learning library dedicated to Touch Processing
  (Based on PyTorch)
• Hardware-agnostic abstractions for rapid experimentation
• Platform for standardizing evaluation and comparison of different models
• Touch "as-a-service"
  • Allows non-ML-experts to use SOTA ML models in their applications
  • Pre-trained models (e.g., Touch detection and slip for DIGIT)
    • (Can also be used for fast fine-tuning)
• Native integration with DIGIT (hardware) and Tacto (simulator)
• (Working on supporting non vision-based sensors)
• Open-source -- Anybody can contribute

Lambeta, M.; Xu, H.; Xu, J.; Chou, P.-W.; Wang, S.; Darrell, T. & Calandra, R.
PyTouch: A Machine Learning Library for Touch Processing
IEEE International Conference on Robotics and Automation (ICRA), 2021, Online: https://arxiv.org/abs/2105.12791

Code Example

Learning from Multiple Sensors Improve
Performance (Touch Detection Task)

Lambeta, M.; Xu, H.; Xu, J.; Chou, P.-W.; Wang, S.; Darrell, T. & Calandra, R.
PyTouch: A Machine Learning Library for Touch Processing
IEEE International Conference on Robotics and Automation (ICRA), 2021, Online: https://arxiv.org/abs/2105.12791

Making Touch Sensing Ubiquitous

Benchmarks & Datasets

Learning Grasp Stability

Wang, S.; Lambeta, M.; Chou, P.-W. & Calandra, R.
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
Under Review, 2020, Online: https://arxiv.org/abs/2012.08456

Making Touch Sensing Ubiquitous

3D Reconstruction from Vision and Touch

Smith, E. J.; Calandra, R.; Romero, A.; Gkioxari, G.; Meger, D.; Malik, J. & Drozdzal, M.
3D Shape Reconstruction from Vision and Touch
Advances in Neural Information Processing Systems (NeurIPS), 2020, online: https://arxiv.org/abs/2007.03778

Learning to Play Piano with Touch

Xu, H.; Luo, Y.; Wang, S.; Darrell, T. & Calandra, R.
Towards Learning to Play Piano with Dexterous Hands and Touch
Under Review, 2021, online: https://arxiv.org/abs/2106.02040

Learned Compositional Policy

Xu, H.; Luo, Y.; Wang, S.; Darrell, T. & Calandra, R.
Towards Learning to Play Piano with Dexterous Hands and Touch
Under Review, 2021, online: https://arxiv.org/abs/2106.02040

Making Touch Sensing Ubiquitous

Slack Workplace

www.touch-sensing.org

To Conclude

Human Collaborators

Overview

• Touch is a key sensor modality for humans and robots
• Our goal is create an ecosystem that simplify integration and enable to perceive, understand, and interact through touch
  • Hardware
  • Simulation
  • Touch Processing
  • Benchmarks and Datasets
  • Applications
• We want to grow and support Community
• Towards the long-term goal of making touch sensing practical and more accessible (both in research and in the real-world)

Backup Slides

References (of our work on touch sensing)

• Wang, S.; Lambeta, M.; Chou, L. & Calandra, R.
  TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
  Under Review, 2020, Online: https://arxiv.org/abs/2012.08456

• Lambeta, M.; Xu, H.; Xu, J.; Chou, P.-W.; Wang, S.; Darrell, T. & Calandra, R.
  A Machine Learning Framework for Touch Processing
  Under Review, 2020
• Smith, E. J.; Calandra, R.; Romero, A.; Gkioxari, G.; Meger, D.; Malik, J. & Drozdzal, M.
  3D Shape Reconstruction from Vision and Touch
  Advances in Neural Information Processing Systems (NeurIPS), 2020
• Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
  DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
  IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845
• Padmanabha, A.; Ebert, F.; Tian, S.; Calandra, R.; Finn, C. & Levine, S.
  OmniTact: A Multi-Directional High-Resolution Touch Sensor
  IEEE International Conference on Robotics and Automation (ICRA), 2020
• Lin, J.; Calandra, R. & Levine, S.
  Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching
  IEEE International Conference on Robotics and Automation (ICRA), 2019, 3644-3650
• Tian, S.; Ebert, F.; Jayaraman, D.; Mudigonda, M.; Finn, C.; Calandra, R. & Levine, S.
  Manipulation by Feel: Touch-Based Control with Deep Predictive Models
  IEEE International Conference on Robotics and Automation (ICRA), 2019, 818-824
• Calandra, R.; Owens, A.; Jayaraman, D.; Yuan, W.; Lin, J.; Malik, J.; Adelson, E. H. & Levine, S.
  More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
  IEEE Robotics and Automation Letters (RA-L), 2018, 3, 3300-3307
• Calandra, R.; Owens, A.; Upadhyaya, M.; Yuan, W.; Lin, J.; Adelson, E. H. & Levine, S.
  The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
  Conference on Robot Learning (CORL), 2017, 314-323
• Yi, Z.; Calandra, R.; Veiga, F. F.; van Hoof, H.; Hermans, T.; Zhang, Y. & Peters, J.
  Active Tactile Object Exploration with Gaussian Processes
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, 4925-4930
• Calandra, R.; Ivaldi, S.; Deisenroth, M. P.; Rueckert, E. & Peters, J.
  Learning Inverse Dynamics Models with Contacts
  IEEE International Conference on Robotics and Automation (ICRA), 2015, 3186-3191
• Calandra, R.; Ivaldi, S.; Deisenroth, M. P. & Peters, J.
  Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin
  IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), 2015, 690-695

Human Collaborators

From the lab of Dr. Ronald Johansson, Dept. of Physiology, University of Umea, Sweden

The Importance of Touch (in Humans)

What do we want to achieve?

• Spearhead advances in Tactile Sensing

• Develop touch-based applications. Among others:
  • Robotics (e.g., Manipulation)
  • AR/VR (e.g., the social aspect of touch)

Achieving Human-level Manipulation with Robots

Learning to Manipulate a Marble

Marble Manipulation Results

Motivation

Why Robots?

State of the Art in Robotics

From YouTube: https://www.youtube.com/watch?v=g0TaYhjpOfo

What are we missing?

Tactile Sensors in Robotics

Important factors:

• Availability
• Cost
• Form factor
• Capabilities
  (e.g., what is measured, resolution)
• Reliability

Many many sensors in the literature:

• Most are prototypes
• A handful are commercially available
  or can be easily manufactured

[Wilson et al., 2019]

[Piacenza et al., 2020]

[ Fischel et al., 2012]

[Zhang et al., 2018]

[Church et al., 2019]

Traditional Sensors

Cannata, G.; Maggiali, M.; Metta, G. & Sandini, G.
An embedded artificial skin for humanoid robots
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2008, 434-438

• Resistive or Capacitive technology
• Several limitations:
  • Usually, measure force or orthogonal component of force
  • Relatively low density
  • Usually, low-dimensional (i.e., <100) due to cost, mechanical and communication reasons
  • Often need to be calibrated

Vision-based Tactile Sensors

[Kamiyama, K.; Kajimoto, H.; Kawakami, N. & Tachi, S. Evaluation of a vision-based tactile sensor IEEE International Conference on Robotics and Automation (ICRA), 2004, 2, 1542-1547 ]

[Johnson, M. K. & Adelson, E. H. Retrographic sensing for the measurement of surface texture and shape Computer Vision and Pattern Recognition (CVPR), 2009, 1070-1077]

[Abad, A. C. & Ranasinghe, A. Visuotactile Sensors With Emphasis on GelSight Sensor: A Review IEEE Sensors Journal, 2020, 20, 7628-7638]

Credit:
[Yuan, W.; Dong, S. & Adelson, E. H. GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force Sensors, Multidisciplinary Digital Publishing Institute, 2017]

Elastomer Robustness

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Design

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Replaceable Elastomer

Reflective

Reflective
+
Markers

Transparent
+
Markers

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Some Touch Sensing Applications

Predicting Good Grasps

Learning how to Grasp

Active Tactile Exploration

3D Reconstruction from Vision and Touch

Identify Objects from Touch

Learning Grasp Stability

Calandra, R.; Owens, A.; Upadhyaya, M.; Yuan, W.; Lin, J.; Adelson, E. H. & Levine, S.
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
Conference on Robot Learning (CORL), 2017, 314-323

Results

Self-supervised Data Collection

• Setting:
  • 7-DOF Sawyer arm
  • Weiss WSG-50 Parallel gripper
  • one GelSight on each finger
  • Two RGB-D cameras in front and on top

• (Almost) fully autonomous data collection:
  • Estimates the object position using depth, and perform a random grasp of the object.
  • Labels automatically generated by looking at the presence of contacts after each attempted lift

Examples of Training Objects

Collected 6450 grasps from over 60 training objects over ~2 weeks.

Visuo-tactile Learned Model

Calandra, R.; Owens, A.; Jayaraman, D.; Yuan, W.; Lin, J.; Malik, J.; Adelson, E. H. & Levine, S.
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
IEEE Robotics and Automation Letters (RA-L), 2018, 3, 3300-3307

Grasp Success on Unseen Objects

83.8% grasp success on 22 unseen objects
(using only vision yields 56.6% success rate)

Gentle Grasping

• Since our model considers forces, we can select grasps that are effective, but gentle
• Reduces the amount of force used by 50%, with no significant loss in grasp success

Calandra, R.; Owens, A.; Jayaraman, D.; Yuan, W.; Lin, J.; Malik, J.; Adelson, E. H. & Levine, S.
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
IEEE Robotics and Automation Letters (RA-L), 2018, 3, 3300-3307

Touch Sensing for Tele-operation

Fritsche, L.; Unverzagt, F.; Peters, J. & Calandra, R.
First-Person Tele-Operation of a Humanoid Robot
IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), 2015, 997-1002

Learning Fine In-finger Manipulation

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Model-based Reinforcement Learning

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845

Marble Manipulation

Lambeta, M.; Chou, P.-W.; Tian, S.; Yang, B.; Maloon, B.; Most, V. R.; Stroud, D.; Santos, R.; Byagowi, A.; Kammerer, G.; Jayaraman, D. & Calandra, R.
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
IEEE Robotics and Automation Letters (RA-L), 2020, 5, 3838-3845