IART: Learning from Demonstration for Assisted Robotic Therapy Using LSTM

Shrey Pareek, Thenkurussi Kesavadas

Research output: Contribution to journalArticlepeer-review


In this letter, we present an intelligent Assistant for Robotic Therapy (iART), that provides robotic assistance during 3D trajectory tracking tasks. We propose a novel LSTM-based robot learning from demonstration (LfD) paradigm to mimic a therapist's assistance behavior. iART presents a trajectory agnostic LfD routine that can generalize learned behavior from a single trajectory to any 3D shape. Once the therapist's behavior has been learned, iART enables the patient to modify this behavior as per their preference. The system requires only a single demonstration of 2 minutes and exhibits a mean accuracy of 91.41% in predicting, and hence mimicking a therapist's assistance behavior. The system delivers stable assistance in realtime and successfully reproduces different types of assistance behaviors.

Original languageEnglish (US)
Article number8939469
Pages (from-to)477-484
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2020


  • AI-based methods
  • Rehabilitation robotics
  • deep learning in robotics and automation
  • learning from demonstration

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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