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Hierarchical HMM Based Learning of Navigation Primitives for Cooperative Robotic Endovascular Catheterization

  • Hedyeh Rafii-Tari
  • Jindong Liu
  • Christopher J. Payne
  • Colin Bicknell
  • Guang-Zhong Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.

Keywords

State Transition Probability Primitive Motion Operator Input Future Movement Cognitive Workload 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hedyeh Rafii-Tari
    • 1
  • Jindong Liu
    • 1
  • Christopher J. Payne
    • 1
  • Colin Bicknell
    • 2
  • Guang-Zhong Yang
    • 1
  1. 1.The Hamlyn Centre for Robotic SurgeryImperial College LondonUK
  2. 2.Academic Division of SurgeryImperial College LondonUK

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