Iyengar, Keshav;
Sadati, SM Hadi;
Bergeles, Christos;
Spurgeon, Sarah;
Stoyanov, Danail;
(2023)
Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots.
IEEE Robotics and Automation Letters
pp. 1-8.
10.1109/lra.2023.3303714.
(In press).
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Abstract
Concentric Tube Robots (CTRs) are promising for minimally invasive interventions due to their miniature diameter, high dexterity, and compliance with soft tissue. CTRs comprise individual pre-curved tubes usually composed of NiTi and are arranged concentrically. As each tube is relatively rotated and translated, the backbone elongates, twists, and bends with a dexterity that is advantageous for confined spaces. Tube interactions, unmodelled phenomena, and inaccurate tube parameter estimation make physical modeling of CTRs challenging, complicating in turn kinematics and control. Deep reinforcement learning (RL) has been investigated as a solution. However, hardware validation has remained a challenge due to differences between the simulation and hardware domains. With simulation-only data, in this work, domain randomization is proposed as a strategy for translation to hardware of a simulation policy with no additionally acquired physical training data. The differences in simulation and hardware forward kinematics accuracy and precision are characterized by errors of 14.74±8.87 mm or 26.61±17.00 % robot length. We showcase that the proposed domain randomization approach reduces errors by 56 % in mean errors as compared to no domain randomization. Furthermore, we demonstrate path following capability in hardware with a line path with resulting errors of 4.37±2.39 mm or 5.61±3.11 % robot length.
Type: | Article |
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Title: | Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/lra.2023.3303714 |
Publisher version: | https://doi.org/10.1109/LRA.2023.3303714 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10175097 |
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