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Katherine J. Kuchenbecker

14 accepted papers

2025

Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing

Maria-Paola Forte, Nikos Athanasiou, Giulia Ballardini, Jan Ulrich Bartels, Katherine J. Kuchenbecker, Michael J. Black
ICCV 2025poster

Capturing accurate 3D human pose in the wild would provide valuable data for training motion-generation and pose-estimation methods. While video-based estimation approaches have become increasingly accurate, they often fail in common scenarios involving self-contact, such as a hand touching the face…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Diffusion-Based Approximate MPC: Fast and Consistent Imitation of Multi-Modal Action Distributions

Pau Marquez Julbe, Julian Nubert, Henrik Hose, Sebastian Trimpe, Katherine J. Kuchenbecker
IROS 2025

Approximating model predictive control (MPC) using imitation learning (IL) allows for fast control without solving expensive optimization problems online. However, methods that use neural networks in a simple L2-regression setup fail to approximate multi-modal (set-valued) solution distributions cau

Cited by 7Source ↗Scholar ↗
2025

Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand With Low-Resolution in-Hand Tactile Sensing

Lukas Mack, Felix Grüninger, Benjamin A. Richardson, Regine Lendway, Katherine J. Kuchenbecker, Joerg Stueckler
ICRA 2025

Accurate 3D pose estimation of grasped objects is an important prerequisite for robots to perform assembly or in-hand manipulation tasks, but object occlusion by the robot's own hand greatly increases the difficulty of this perceptual task. Here, we propose that combining visual information and prop

Cited by 2Source ↗Scholar ↗
2023

Reconstructing Signing Avatars From Video Using Linguistic Priors

Maria-Paola Forte, Peter Kulits, Chun-Hao P. Huang, Vasileios Choutas, Dimitrios Tzionas, Katherine J. Kuchenbecker +1
CVPR 2023poster

Sign language (SL) is the primary method of communication for the 70 million Deaf people around the world. Video dictionaries of isolated signs are a core SL learning tool. Replacing these with 3D avatars can aid learning and enable AR/VR applications, improving access to technology and online media…

Cited by 15Source ↗PDF ↗Scholar ↗
2021

Robotics for Occupational Therapy: Learning Upper-Limb Exercises From Demonstrations

Siyao Hu, Rochelle Mendonca, Michelle J. Johnson, Katherine J. Kuchenbecker
RA-L 2021

We describe a learning-from-demonstration technique that enables a general-purpose humanoid robot to lead a user through object-mediated upper-limb exercises. It needs only tens of seconds of training data from a therapist teleoperating the robot to do the task with the user. We model the robot beha

Cited by 6Source ↗Scholar ↗
2021

Sensorimotor-inspired Tactile Feedback and Control Improve Consistency of Prosthesis Manipulation in the Absence of Direct Vision

Neha Thomas, Farimah Fazlollahi, Jeremy D. Brown, Katherine J. Kuchenbecker
IROS 2021poster

The lack of haptically aware upper-limb prostheses forces amputees to rely largely on visual cues to complete activities of daily living. In contrast, non-amputees inherently rely on conscious haptic perception and automatic tactile reflexes to govern volitional actions in situations that do not all…

Cited by 12Source ↗Scholar ↗
2020

Calibrating a Soft ERT-Based Tactile Sensor with a Multiphysics Model and Sim-to-real Transfer Learning

Hyosang Lee, Hyunkyu Park, Gokhan Serhat, Huanbo Sun, Katherine J. Kuchenbecker
ICRA 2020poster

Tactile sensors based on electrical resistance tomography (ERT) have shown many advantages for implementing a soft and scalable whole-body robotic skin; however, calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating…

Cited by 21Source ↗Scholar ↗
2019

Improving Haptic Adjective Recognition with Unsupervised Feature Learning

Benjamin A. Richardson, Katherine J. Kuchenbecker
ICRA 2019poster

Humans can form an impression of how a new object feels simply by touching its surfaces with the densely innervated skin of the fingertips. Many haptics researchers have recently been working to endow robots with similar levels of haptic intelligence, but these efforts almost always employ hand-craf…

Cited by 23Source ↗Scholar ↗
2019

Internal Array Electrodes Improve the Spatial Resolution of Soft Tactile Sensors Based on Electrical Resistance Tomography

Hyosang Lee, Kyungseo Park, Jung Kim, Katherine J. Kuchenbecker
ICRA 2019poster

Robots operating in unstructured environments would benefit from soft whole-body tactile sensors, but implementing such systems typically requires complex electrical wiring to a large number of sensing elements. The reconstruction method called electrical resistance tomography (ERT) has shown promis…

Cited by 24Source ↗Scholar ↗
2018

Task-Driven PCA-Based Design Optimization of Wearable Cutaneous Devices

Claudio Pacchierotti, Eric M. Young, Katherine J. Kuchenbecker
RA-L 2018

Small size and low weight are critical requirements for wearable and portable haptic interfaces, making it essential to work toward the optimization of their sensing and actuation systems. This letter presents a new approach for task-driven design optimization of fingertip cutaneous haptic devices.

Cited by 27Source ↗Scholar ↗
2017

Proton 2: Increasing the sensitivity and portability of a visuo-haptic surface interaction recorder

Alex Burka, Abhinav Rajvanshi, Sarah Allen, Katherine J. Kuchenbecker
ICRA 2017poster

The Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short) is a new handheld visuo-haptic sensing system that records surface interactions. We previously demonstrated system calibration and a classification task using external motion tracking. This paper details impro…

Cited by 10Source ↗Scholar ↗
2016

Deep learning for tactile understanding from visual and haptic data

Yang Gao, Lisa Anne Hendricks, Katherine J. Kuchenbecker, Trevor Darrell
ICRA 2016

Robots which interact with the physical world will benefit from a fine-grained tactile understanding of objects and surfaces. Additionally, for certain tasks, robots may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose

Cited by 262Source ↗Scholar ↗