Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols-Reference-Cited by-同舟云学术

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Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols

Published:2016-01 Issue: Volume:75 Page:260-272
ISSN:0921-8890
Container-title:Robotics and Autonomous Systems
language:en
Short-container-title:Robotics and Autonomous Systems

Author:

Takano Wataru,Nakamura Yoshihiko

Funder

Japan Society for the Promotion of Science

Ministry of Internal Affairs and Communications

Publisher

Elsevier BV

Subject

Computer Science Applications,General Mathematics,Software,Control and Systems Engineering

Reference28 articles.

1. Learning by watching: Extracting reusable task knowledge from visual observation of human performance;Kuniyoshi;IEEE Trans. Robot. Autom.,1994

2. Hierarchical reinforcement learning for motion learning: learning “stand-up” trajectories;Morimoto;Adv. Robot.,1999

3. Getting humaniods to move and imitate;Mataric;IEEE Intell. Syst.,2000

4. MOSAIC model for sensorimotor learning and control;Haruno;Neural Comput.,2001

5. Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment;Tani;IEEE Trans. Syst. Man Cybern. A,2003

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1. Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Spatio-temporal categorization for first-person-view videos using a convolutional variational autoencoder and Gaussian processes;Frontiers in Robotics and AI;2022-09-30

3. Understanding and Segmenting Human Demonstrations into Reusable Compliant Primitives;2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2021-09-27

4. Review of the techniques used in motor‐cognitive human‐robot skill transfer;Cognitive Computation and Systems;2021-05-30

5. Human interaction behavior modeling using Generative Adversarial Networks;Neural Networks;2020-12

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