Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre. 

Citation

BibTex format

@article{Borvorntanajanya:2026:10.1109/lra.2026.3693538,
author = {Borvorntanajanya, K and Leung, FF and Shi, J and Franco, E and Chiu, PWY and Yam, Y and Baena, FRY},
doi = {10.1109/lra.2026.3693538},
journal = {IEEE Robotics and Automation Letters},
pages = {8284--8291},
title = {Data-efficient modeling of hysteresis and crosstalk for inverse kinematics of soft manipulators},
url = {http://dx.doi.org/10.1109/lra.2026.3693538},
volume = {11},
year = {2026}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Borvorntanajanya,K
AU  - Leung,FF
AU  - Shi,J
AU  - Franco,E
AU  - Chiu,PWY
AU  - Yam,Y
AU  - Baena,FRY
DO  - 10.1109/lra.2026.3693538
EP  - 8291
PY  - 2026///
SN  - 2377-3766
SP  - 8284
TI  - Data-efficient modeling of hysteresis and crosstalk for inverse kinematics of soft manipulators
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/lra.2026.3693538
UR  - https://doi.org/10.1109/lra.2026.3693538
VL  - 11
ER  -
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