Data-efficient modeling of hysteresis and crosstalk for inverse kinematics of soft manipulators
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Nonlinearities in soft continuum manipulators, arising from material hysteresis and intersegmental coupling in multi-segment robots, present significant challenges for accurate open-loop inverse kinematics (IK) control. In particular, morphable pneumatic chambers adjust their shape and stiffness with internal pressure, increasing force output but also introducing nonlinearities that complicate control. This paper introduces a sequence based machine learning approach that is data-efficient, modeling and compensating for both hysteresis and crosstalk in systems with morphable chambers. Through systematic comparison of Long Short-Term Memory (LSTM) and Transformer architectures under data-limited conditions, we demonstrate the effectiveness of sequence-based models in capturing temporal dependencies. We then propose a Recursive Segment-wise Crosstalk Compensation
(RSCC) pipeline that decomposes control of multi-segment robots into independent single-segment subproblems, with each constituent model trained using 500 samples. Applied to a two-segment morphable-chamber manipulator, RSCC achieves approximately 11% normalized positional error and outperforms a monolithic multi-segment LSTM baseline trained on 2000 samples within the same workspace, highlighting its potential for precise open-loop control in minimally invasive surgical applications.
(RSCC) pipeline that decomposes control of multi-segment robots into independent single-segment subproblems, with each constituent model trained using 500 samples. Applied to a two-segment morphable-chamber manipulator, RSCC achieves approximately 11% normalized positional error and outperforms a monolithic multi-segment LSTM baseline trained on 2000 samples within the same workspace, highlighting its potential for precise open-loop control in minimally invasive surgical applications.
Date Issued
2026-07-01
Date Acceptance
2026-05-02
Citation
IEEE Robotics and Automation Letters, 2026, 11 (7), pp.8284-8291
ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers
Start Page
8284
End Page
8291
Journal / Book Title
IEEE Robotics and Automation Letters
Volume
11
Issue
7
Copyright Statement
Copyright © 2026 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2026-05-14