ArticleFrontiers in neurorobotics2020
An Incremental Learning Framework to Enhance Teaching by Demonstration Based on Multimodal Sensor Fusion.
Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Design of an Adaptive Model for Intelligent Car Seats Based on Human Pose Recognition.Annals of the New York Academy of Sciences · 2026Article
- A multimodal human-robot sign language interaction framework applied in social robots.Frontiers in neuroscience · 2023Article
- Hand-Object Interaction: From Human Demonstrations to Robot Manipulation.Frontiers in robotics and AI · 2021Article
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Authors and funding
4 authors.
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Abstract
Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demonstration task accurately. The multimodal demonstration data are collected from two different kinds of sensors in the demonstration phase. Then, the Kalman filter (KF) and dynamic time warping (DTW) algorithms are used to preprocessing the data for the multiple sensor signals. The KF algorithm is mainly used to fuse sensor data of different modalities, and the DTW algorithm is used to align the data in the same timeline. The preprocessed demonstration data are further trained and learned by the incremental learning network and sent to a Baxter robot for reproducing the task demonstrated by the human. Comparative experiments have been performed to verify the effectiveness of the proposed framework.
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