Research

Designing Variable Stiffness Profiles To Optimize The Physical Human Robot Interface Of Hand Exoskeletons

August 26, 2018

Abstract

The design of comfortable and effective physical human robot interaction (pHRI) interfaces for force transfer is a prominent challenge for coupled human-robot systems. Forces applied by the robot at the fingers create reaction forces on the dorsal surface of the hand, often leading to high pressure concentrations which can cause pain and discomfort. In this paper, the interaction between the pHRI interface and the dorsal surface of the hand is systematically characterized, and a new method for the design of comfortable interfaces is presented. The variability of the stiffness of the hand dorsum is quantified experimentally, and this data is used to minimize the peak pressure exerted on the hand dorsum, by varying the stiffness profile of the pHRI interface. This optimized design is demonstrated to improve the pressure distribution over the hand dorsum where the robot is attached to the hand. Additionally, to enable informed design choices, the effects of varying the stiffness of the pHRI interface on relative displacement between the robot and the hand dorsum are also characterized. This optimization approach to designing pHRI interface can be extended to different limbs, especially when there is a transfer of high moment loads to the human body, provided the appropriate stiffness data is available.

Download the Paper

Related Publications

November 27, 2022

Core Machine Learning

Neural Attentive Circuits

Nicolas Ballas, Bernhard Schölkopf, Chris Pal, Francesco Locatello, Li Erran, Martin Weiss, Nasim Rahaman, Yoshua Bengio

November 27, 2022

November 27, 2022

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann

November 27, 2022

November 16, 2022

NLP

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Kushal Tirumala, Aram H. Markosyan, Armen Aghajanyan, Luke Zettlemoyer

November 16, 2022

November 10, 2022

Computer Vision

Learning State-Aware Visual Representations from Audible Interactions

Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado

November 10, 2022

April 08, 2021

Responsible AI

Integrity

Towards measuring fairness in AI: the Casual Conversations dataset

Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer

April 08, 2021

April 30, 2018

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings | Facebook AI Research

Tomer Galanti, Lior Wolf, Sagie Benaim

April 30, 2018

April 30, 2018

Computer Vision

NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

December 11, 2019

Speech & Audio

Computer Vision

Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research

Eliya Nachmani, Lior Wolf

December 11, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.