2026-09-19T16:25:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1061232026-06-06T00:55:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniela Rus.
Homberg, Bianca (Bianca S.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T16:29:55Z
2016-12-22T16:29:55Z
2016
2016
http://hdl.handle.net/1721.1/106123
965800025
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 85-88).
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 hand can be configured in different ways using finger unit modules. 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. This hand is incorporated into a full system with vision and motion planning on the Baxter robot to autonomously perform grasps of objects placed on a table. This hand is a first step towards proprioceptive soft grasping.
by Bianca Homberg.
M.Eng.
115 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Robust proprioceptive grasping with a soft robot hand
Thesis
application/pdf
Publication
unknown
unknown
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<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 hand can be configured in different ways using finger unit modules. 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. This hand is incorporated into a full system with vision and motion planning on the Baxter robot to autonomously perform grasps of objects placed on a table. This hand is a first step towards proprioceptive soft grasping.</Abstract>
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