Katherine Kuchenbecker

Director

Haptic Intelligence

Office: 5P5
Heisenbergstr. 3
70569 Stuttgart
Germany

+49 711 689-3510

+49 711 689-3512

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Overview
Publications

Katherine J. Kuchenbecker directs the Haptic Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany. She was previously an Associate Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, where she held the Class of 1940 Bicentennial Endowed Term Chair and a secondary appointment in Computer and Information Science. Kuchenbecker earned her Ph.D. in Mechanical Engineering at Stanford University in 2006 and did a postdoctoral fellowship at the Johns Hopkins University. Her research centers on haptic interfaces, which enable a user to touch virtual and distant objects as though they were real and within reach, as well as haptic sensing systems, which allow robots to physically interact with objects and people.

Kuchenbecker delivered a TEDYouth talk on haptics in 2012, and she has received several honors including a 2009 NSF CAREER Award, the 2012 IEEE Robotics and Automation Society Academic Early Career Award, and a 2014 Penn Lindback Award for Distinguished Teaching. Her team has won various best paper and best demonstration awards, and she frequently gives keynote talks at conferences. She was co-chair of the IEEE Technical Committee on Haptics from 2014 to 2017, and she co-chaired the IEEE Haptics Symposium in 2016 and 2018.

192 results (View BibTeX file of all listed publications)

2018

Statistical Modelling of Fingertip Deformations and Contact Forces during Tactile Interaction

Gueorguiev, D., Tzionas, D., Pacchierotti, C., Black, M. J., Kuchenbecker, K. J.

Extended abstract presented at the 2018 CSF Hand, Brain and Technology conference (HBT), August 2018 (misc)

Abstract

Little is known about the shape and properties of the human finger during haptic interaction, even though these are essential parameters for controlling wearable finger devices and deliver realistic tactile feedback. This study explores a framework for four-dimensional scanning (3D over time) and modelling of finger-surface interactions, aiming to capture the motion and deformations of the entire finger with high resolution while simultaneously recording the interfacial forces at the contact. Preliminary results show that when the fingertip is actively pressing a rigid surface, it undergoes lateral expansion and proximal/distal bending, deformations that cannot be captured by imaging of the contact area alone. Therefore, we are currently capturing a dataset that will enable us to create a statistical model of the finger’s deformations and predict the contact forces induced by tactile interaction with objects. This technique could improve current methods for tactile rendering in wearable haptic devices, which rely on general physical modelling of the skin’s compliance, by developing an accurate model of the variations in finger properties across the human population. The availability of such a model will also enable a more realistic simulation of virtual finger behaviour in virtual reality (VR) environments, as well as the ability to accurately model a specific user’s finger from lower resolution data. It may also be relevant for inferring the physical properties of the underlying tissue from observing the surface mesh deformations, as previously shown for body tissues.

Project Page [BibTex]

2018

Gueorguiev, D., Tzionas, D., Pacchierotti, C., Black, M. J., Kuchenbecker, K. J. Statistical Modelling of Fingertip Deformations and Contact Forces during Tactile Interaction Extended abstract presented at the 2018 CSF Hand, Brain and Technology conference (HBT), August 2018 (misc)

Project Page [BibTex]

Teaching a Robot Bimanual Hand-Clapping Games via Wrist-Worn IMUs

Fitter, N. T., Kuchenbecker, K. J.

Frontiers in Robotics and Artificial Intelligence, 5(85), July 2018 (article)

Abstract

Colleagues often shake hands in greeting, friends connect through high fives, and children around the world rejoice in hand-clapping games. As robots become more common in everyday human life, they will have the opportunity to join in these social-physical interactions, but few current robots are intended to touch people in friendly ways. This article describes how we enabled a Baxter Research Robot to both teach and learn bimanual hand-clapping games with a human partner. Our system monitors the user's motions via a pair of inertial measurement units (IMUs) worn on the wrists. We recorded a labeled library of 10 common hand-clapping movements from 10 participants; this dataset was used to train an SVM classifier to automatically identify hand-clapping motions from previously unseen participants with a test-set classification accuracy of 97.0%. Baxter uses these sensors and this classifier to quickly identify the motions of its human gameplay partner, so that it can join in hand-clapping games. This system was evaluated by N = 24 naïve users in an experiment that involved learning sequences of eight motions from Baxter, teaching Baxter eight-motion game patterns, and completing a free interaction period. The motion classification accuracy in this less structured setting was 85.9%, primarily due to unexpected variations in motion timing. The quantitative task performance results and qualitative participant survey responses showed that learning games from Baxter was significantly easier than teaching games to Baxter, and that the teaching role caused users to consider more teamwork aspects of the gameplay. Over the course of the experiment, people felt more understood by Baxter and became more willing to follow the example of the robot. Users felt uniformly safe interacting with Baxter, and they expressed positive opinions of Baxter and reported fun interacting with the robot. Taken together, the results indicate that this robot achieved credible social-physical interaction with humans and that its ability to both lead and follow systematically changed the human partner's experience.

DOI [BibTex]

Fitter, N. T., Kuchenbecker, K. J. Teaching a Robot Bimanual Hand-Clapping Games via Wrist-Worn IMUs Frontiers in Robotics and Artificial Intelligence, 5(85), July 2018 (article)

DOI [BibTex]

Reducing 3D Vibrations to 1D in Real Time

Park, G., Kuchenbecker, K. J.

Pisa, Italy, June 2018, Hands-on demonstration presented at EuroHaptics (misc)

Abstract

In this demonstration, you will hold two pen-shaped modules: an in-pen and an out-pen. The in-pen is instrumented with a high-bandwidth three-axis accelerometer, and the out-pen contains a one-axis voice coil actuator. Use the in-pen to interact with different surfaces; the measured 3D accelerations are continually converted into 1D vibrations and rendered with the out-pen for you to feel. You can test conversion methods that range from simply selecting a single axis to applying a discrete Fourier transform or principal component analysis for realistic and brisk real-time conversion.