Katherine Kuchenbecker

Director

Haptic Intelligence

Office: 5P5
Heisenbergstr. 3
70569 Stuttgart
Germany

+49 711 689-3510

+49 711 689-3512

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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.

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192 results (View BibTeX file of all listed publications)

2018

Softness, Warmth, and Responsiveness Improve Robot Hugs

Block, A. E., Kuchenbecker, K. J.

International Journal of Social Robotics, October 2018 (article)

Abstract

Hugs are one of the first forms of contact and affection humans experience. Due to their prevalence and health benefits, roboticists are naturally interested in having robots one day hug humans as seamlessly as humans hug other humans. This project's purpose is to evaluate human responses to different robot physical characteristics and hugging behaviors. Specifically, we aim to test the hypothesis that a soft, warm, touch-sensitive PR2 humanoid robot can provide humans with satisfying hugs by matching both their hugging pressure and their hugging duration. Thirty relatively young and rather technical participants experienced and evaluated twelve hugs with the robot, divided into three randomly ordered trials that focused on physical robot characteristics (single factor, three levels) and nine randomly ordered trials with low, medium, and high hug pressure and duration (two factors, three levels each). Analysis of the results showed that people significantly prefer soft, warm hugs over hard, cold hugs. Furthermore, users prefer hugs that physically squeeze them and release immediately when they are ready for the hug to end. Taking part in the experiment also significantly increased positive user opinions of robots and robot use.

link (url) DOI Project Page [BibTex]

2018

Block, A. E., Kuchenbecker, K. J. Softness, Warmth, and Responsiveness Improve Robot Hugs International Journal of Social Robotics, October 2018 (article)

link (url) DOI Project Page [BibTex]

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.