Robust proprioceptive grasping with a soft robot hand
Name
10514_2018_Article_9754.pdf
Size
3.24 MB
Format
Adobe PDF
Checksum (MD5)
0751270d4508f1e0a296549898880155
Author(s)
Homberg, Bianca S.
Katzschmann, Robert Kevin
Dogar, MehmetRemzi
Rus, Daniela L
Date Issued
April 2018
Journal
Autonomous Robots
Publisher
Springer-Verlag
Citation
Homberg, Bianca S. et al. “Robust Proprioceptive Grasping with a Soft Robot Hand.” Autonomous Robots (April 2018): 1-16 © 2018 The Author(s)
Version
Final published version
Abstract
This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements along with a combined system which autonomously performs grasps. A highly compliant soft hand allows for intrinsic robustness to grasping uncertainties; the addition of internal sensing allows the configuration of the hand and object to be detected. The finger module includes resistive force sensors on the fingertips for contact detection and resistive bend sensors for measuring the curvature profile of the finger. The curvature sensors can be used to estimate the contact geometry and thus to distinguish between a set of grasped objects. With one data point from each finger, the object grasped by the hand can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. A closed loop system uses a camera to detect approximate object locations. Compliance in the soft hand handles that uncertainty in addition to geometric uncertainty in the shape of the object.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10514-018-9754-1
