Perceiving, Understanding, and Interacting through Touch

Roberto Calandra

ML in PL - 08 November 2024

Learning, Adaptive Systems, and Robotics (LASR) Lab

Motivation

How to scale to more complex, unstructured domains?

Robotics

Finance

Biological Sciences

Logistics /
Decision Making

Why Robots?

Disaster Relief

Industrial Automation

Exploration

Medicine & Eldercare

State of the Art in Robotics

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

What are we missing?

Key Challenges

  • Multi-modal Sensing

  • Adaptive Hardware configuration

  • Quick adaptation to new tasks

 

Touch Sensing

Morphological adaptation

In this talk

Model-based
Reinforcement Learning

Hardware

Software

The Importance of Touch

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

The Importance of Touch (in Humans)

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?

Standardization, and the creation of an ecosystem of tools

Hardware

Software

The Next Breakthrough will be in Touch

Audio

Touch

Vision

(~1890)

(~1990)

(2020s ?)

Grand Vision

+ Applications

+ Community

Hardware

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]

Vision-based Tactile Sensors

[Hillis, W. D. A High-Resolution Imaging Touch Sensor The International Journal of Robotics Research, 1982, 1, 33-44 ]

[Tanie, K.; Komoriya, K.; Kaneko, M.; Tachi, S. & Fujikawa, A. A high resollution tactile sensor Proc. of 4th Int. Conf. on Robot Vision and Sensory Controls, 1984, 251, 260]

[Begej, S. Planar and finger-shaped optical tactile sensors for robotic applications IEEE Journal on Robotics and Automation, 1988, 4, 472-484]
[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]

Democratizing Touch with 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
taxels

307,200
taxels

Mounted on multi-finger hands

Open-source

https://digit.ml

1000x
Higher resolution

1000x
Cheaper

* component cost for 1000 units, not including labor

DIGIT Commercialization

  • Replicated in 20+ universities

  • Yet, it can still be challenging to manufacture a sensor without mechanical/electrical experience

  • Partnership with GelSight Inc. to commercialize DIGIT

  • Most widespread tactile sensor in robotics!

    • Part of Mitsubishi Electric RAISE (Robotics as an Intelligent Services Ecosystem)

Limitations

  • Sampling Rate
    (Camera are relatively slow)
  • Data Bandwidth
    (Camera generate hundreds of Mb/s)
  • Bulky
    (Focal distance and electronics)
  • ...

OmniTact

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

Evetac

Funk, N.; Helmut, E.; Chalvatzaki, G.; Calandra, R. & Peters, J.
Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation 
IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2312.01236

  • Neuromorphic tactile sensor
  • Event-based
  • Up to 1KHz
  • Modular and using off-the-shelf components
    (Inspired by the DigiTac by Nathan)

Evetac

Funk, N.; Helmut, E.; Chalvatzaki, G.; Calandra, R. & Peters, J.
Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation 
IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2312.01236

Bandwidth

12%

1.7% over
entire
trajectory

Funk, N.; Helmut, E.; Chalvatzaki, G.; Calandra, R. & Peters, J.
Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation 
IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2312.01236

DIGIT Pinky

Di, J.; Dugonjic, Z.; Fu, W.; Wu, T.; Mercado, R.; Sawyer, K.; Most, V. R.; Kammerer, G.; Speidel, S.; Fan, R. E.; Sonn, G.; Cutkosky, M. R.; Lambeta, M. & Calandra, R.
Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors
Accepted to IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2403.05500

DIGIT Pinky

Di, J.; Dugonjic, Z.; Fu, W.; Wu, T.; Mercado, R.; Sawyer, K.; Most, V. R.; Kammerer, G.; Speidel, S.; Fan, R. E.; Sonn, G.; Cutkosky, M. R.; Lambeta, M. & Calandra, R.
Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors
Accepted to IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2403.05500

DIGIT Pinky

Di, J.; Dugonjic, Z.; Fu, W.; Wu, T.; Mercado, R.; Sawyer, K.; Most, V. R.; Kammerer, G.; Speidel, S.; Fan, R. E.; Sonn, G.; Cutkosky, M. R.; Lambeta, M. & Calandra, R.
Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors
Accepted to IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2403.05500

DIGIT Pinky

Di, J.; Dugonjic, Z.; Fu, W.; Wu, T.; Mercado, R.; Sawyer, K.; Most, V. R.; Kammerer, G.; Speidel, S.; Fan, R. E.; Sonn, G.; Cutkosky, M. R.; Lambeta, M. & Calandra, R.
Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors
Accepted to IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2403.05500

Cancer Detection in Prostate Tissue

Cancer

No Cancer

Di, J.; Dugonjic, Z.; Fu, W.; Wu, T.; Mercado, R.; Sawyer, K.; Most, V. R.; Kammerer, G.; Speidel, S.; Fan, R. E.; Sonn, G.; Cutkosky, M. R.; Lambeta, M. & Calandra, R.
Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors
Accepted to IEEE Transactions on Robotics (T-RO), 2024 https://arxiv.org/abs/2403.05500

Digit360: A Superhuman Multi-modal Fingertip

Lambeta M.; Wu T.; Sengül A.; Most V. R.; Black N.; Sawyer K.; Mercado R.; Qi H.; Sohn A.; Taylor B.; Tydingco N.; Kammerer G.; Stroud D.; Khatha J.; Jenkins K.; Most K.; Stein N.; Chavira R.; Craven-Bartle T.; Sanchez E.; Ding Y.; Malik J. & Calandra R.
Digitizing Touch with an Artificial Multimodal Fingertip
Arxiv Preprint. https://arxiv.org/abs/2411.02479

Resolution

Lambeta M.; Wu T.; Sengül A.; Most V. R.; Black N.; Sawyer K.; Mercado R.; Qi H.; Sohn A.; Taylor B.; Tydingco N.; Kammerer G.; Stroud D.; Khatha J.; Jenkins K.; Most K.; Stein N.; Chavira R.; Craven-Bartle T.; Sanchez E.; Ding Y.; Malik J. & Calandra R.
Digitizing Touch with an Artificial Multimodal Fingertip
Arxiv Preprint. https://arxiv.org/abs/2411.02479

Multi-modality

Lambeta M.; Wu T.; Sengül A.; Most V. R.; Black N.; Sawyer K.; Mercado R.; Qi H.; Sohn A.; Taylor B.; Tydingco N.; Kammerer G.; Stroud D.; Khatha J.; Jenkins K.; Most K.; Stein N.; Chavira R.; Craven-Bartle T.; Sanchez E.; Ding Y.; Malik J. & Calandra R.
Digitizing Touch with an Artificial Multimodal Fingertip
Arxiv Preprint. https://arxiv.org/abs/2411.02479

Edge-AI

Lambeta M.; Wu T.; Sengül A.; Most V. R.; Black N.; Sawyer K.; Mercado R.; Qi H.; Sohn A.; Taylor B.; Tydingco N.; Kammerer G.; Stroud D.; Khatha J.; Jenkins K.; Most K.; Stein N.; Chavira R.; Craven-Bartle T.; Sanchez E.; Ding Y.; Malik J. & Calandra R.
Digitizing Touch with an Artificial Multimodal Fingertip
Arxiv Preprint. https://arxiv.org/abs/2411.02479

Simulation

Touch Simulation

Simulators play a crucial role in prototyping, debugging and benchmarking new advances in robotics

  • 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
IEEE Robotics and Automation Letters (RA-L), 2022, Online: https://arxiv.org/abs/2012.08456

Software Architecture

Wang, S.; Lambeta, M.; Chou, P.-W. & Calandra, R.
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
IEEE Robotics and Automation Letters (RA-L), 2022, Online: https://arxiv.org/abs/2012.08456

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
IEEE Robotics and Automation Letters (RA-L), 2022, Online: https://arxiv.org/abs/2012.08456

Sim2Real

Wang, S.; Lambeta, M.; Chou, P.-W. & Calandra, R.
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
IEEE Robotics and Automation Letters (RA-L), 2022, Online: https://arxiv.org/abs/2012.08456

Touch Processing
(i.e., AI for Touch)

Creating a New 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

What Representations do we Need 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

Kerr, J.; Huang, H.; Wilcox, A.; Hoque, R.; Ichnowski, J.; Calandra, R. & Goldberg, K.
Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features
Robotics: Science and Systems (RSS) 2023, Online: https://arxiv.org/pdf/2209.13042

Transfer Across Sensors

Transfer across Tasks

Touch and Language

Fu, L.; Datta, G.; Huang, H.; Panitch, W. C.-H.; Drake, J.; Ortiz, J.; Mukadam, M.; Lambeta, M.; Calandra, R. & Goldberg, K.
A Touch, Vision, and Language Dataset for Multimodal Alignment
Accepted to ICML 2024 https://arxiv.org/abs/2402.13232

Tactile SLAM

Suresh, S.; Qi, H.; Wu, T.; Fan, T.; Pineda, L.; Lambeta, M.; Malik, J.; Kalakrishnan, M.; Calandra, R.; Kaess, M.; Ortiz, J. & Mukadam, M.
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation 
Accepted, To Appear, 2024 https://arxiv.org/abs/2312.13469

2nd Workshop on Touch Processing

www.touchprocessing.org/2024/

15 December 2024 @ NeurIPS
Vancouver, Canada

Applications

Key Applications

Robotics

Metaverse
(AR/VR)

E-commerce

Medical

Some Touch Sensing Applications

Predicting Grasp Stability
[Calandra et al. 2017]

Learning how to (Re)Grasp
[Calandra et al. 2018]

Active Tactile Exploration
[Yi at al. 2016]

3D Reconstruction from Vision and Touch
[Smith et al. 2020; Smith et al. 2021]

Identify Objects from Touch

[Lin et al. 2019]

Learning to Play Piano from Touch
[Xu at al. 2022]

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

Examples of Training Objects

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

Grasp Success on Unseen Objects

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

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

Achieving Human-level Manipulation with Robots

Learning General In-Hand Rotation

Qi, Haozhi, Brent Yi, Sudharshan Suresh, Mike Lambeta, Yi Ma, Roberto Calandra, and Jitendra Malik.
General In-Hand Object Rotation with Vision and Touch.
Conference on Robot Learning (CORL). 2023 https://arxiv.org/abs/2309.09979

Model

Qi, Haozhi, Brent Yi, Sudharshan Suresh, Mike Lambeta, Yi Ma, Roberto Calandra, and Jitendra Malik.
General In-Hand Object Rotation with Vision and Touch.
Conference on Robot Learning (CORL). 2023 https://arxiv.org/abs/2309.09979

Learning General In-Hand Rotation

Qi, Haozhi, Brent Yi, Sudharshan Suresh, Mike Lambeta, Yi Ma, Roberto Calandra, and Jitendra Malik.
General In-Hand Object Rotation with Vision and Touch.
Conference on Robot Learning (CORL). 2023 https://arxiv.org/abs/2309.09979

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
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, online: https://arxiv.org/abs/2106.02040

Telepresence via Haptic Feedback

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

To Conclude

Human Collaborators

LASR Lab @ TU Dresden

Supported by

We are Hiring

www.lasr.org

  • Post-docs
  • PhD Students
  • Robot Lab Manager

Email: Roberto Calandra (rcalandra@lasr.org)

Touch Sensing and Processing Summer School 2025

To Be Announced Soon!

www.lasr.org/tspss2025

Overview

  • Touch is a key sensor modality for humans and robots
  • Presented our ecosystem for research in tactile sensing aimed at simplifying integration and enable to perceive, understand, and interact through touch:
    • Hardware
    • Simulation
    • Touch Processing
  • Towards the long-term goal of making touch sensing practical and more accessible (both in research and in the real-world)
  • Touch sensing is at the verge of a "digital revolution" that will make it ubiquitous and enable new applications

Thank you!

DIGIT

TACTO

PyTouch

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
    IEEE Robotics and Automation Letters (RA-L), 2022, 7, 3930-3937, Online: https://arxiv.org/abs/2012.08456

  • Smith, E. J.; Meger, D.; Pineda, L.; Calandra, R.; Malik, J.; Romero, A. & Drozdzal, M.
    Active 3D Shape Reconstruction from Vision and Touch
    Advances in Neural Information Processing Systems (NeurIPS), 2021
  • 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
  • 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, 618-624
  • 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

Additional Slides

Slack Channel

www.touch-sensing.org

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

In-Hand Object Rotation via Rapid Motor Adaptation

Touch for the Metaverse

  • Touch is a deeply social and emotional sense
  • How can we sense, calibrate and reproduce touch?
  • How can we accurately and easily digitize objects and their physical properties into virtual worlds?

Sense

Process

Reproduce

Telepresence via Haptic Feedback

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

Robot Learning

Code Example

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

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

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

Features of PyTouch

  • First Machine Learning library dedicated to Touch Processing
    (Based on PyTorch)
  • Hardware-agnostic abstractions for rapid experimentation
  • Native integration with DIGIT (hardware) and Tacto (simulator)
    (Working on supporting non vision-based sensors)
  • 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)
  • 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

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
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, online: https://arxiv.org/abs/2106.02040

Do we really need Touch?

Qi, H.; Kumar, A.; Calandra, R.; Ma, Y. & Malik J.
In-Hand Object Rotation via Rapid Motor Adaptation
Conference on Robot Learning (CORL), 2022, https://arxiv.org/abs/2210.04887

Failure Case

Understanding the Learned Model

More force = better grasp

Understanding the Learned Model

But not always ?

Understanding the Learned Model

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

ML in PL

By Roberto Calandra

ML in PL

Touch is a crucial sensor modality in both humans and robots. Recent advances in tactile sensing hardware have resulted -- for the first time -- in the availability of mass-produced, high-resolution, inexpensive, and reliable tactile sensors. In this talk, I will argue for the importance of creating a new computational field of "Touch processing" dedicated to the processing and understanding of touch, similarly to what computer vision is for vision. This new field will present significant challenges both in terms of research and engineering. To start addressing some of these challenges, I will introduce our open-source ecosystem dedicated to touch sensing research. Finally, I will present some applications of touch in robotics and discuss other future applications.

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