iART: Learning from Demonstration for Assisted Robotic Therapy Using LSTM
On the map: Offline RL and imitation learning from data → Robot learning from demonstration
Abstract (arXiv)
In this paper, 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 0 of its 19 references that are in the map. First posted on arXiv 2 Mar 2023.
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- 2022Learning Human Body Motions from Skeleton-Based Observations for Robot-Assisted Therapy ↗
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- 2026Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment ↗
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- 2024Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study ↗