Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics

被引:0
|
作者
Gonzalez-Santocildes, Asier [1 ]
Vazquez, Juan-Ignacio [1 ]
Eguiluz, Andoni [1 ]
机构
[1] Univ Deusto, Fac Engn, Avda Univ 24, Bilbao 48007, Spain
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 14期
基金
欧盟地平线“2020”;
关键词
robotics; applied artificial intelligence; human-robot interaction; human-robot collaboration; agents; VIRTUAL-REALITY; ERROR-DETECTION; CLASSIFICATION;
D O I
10.3390/app14146345
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human-robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human-robot interactions.
引用
下载
收藏
页数:22
相关论文
共 50 条
  • [31] Two-step gradient-based reinforcement learning for underwater robotics behavior learning
    El-Fakdi, Andres
    Carreras, Marc
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2013, 61 (03) : 271 - 282
  • [32] Enhancing variational quantum state diagonalization using reinforcement learning techniques
    Kundu, Akash
    Bedelek, Przemyslaw
    Ostaszewski, Mateusz
    Danaci, Onur
    Patel, Yash J.
    Dunjko, Vedran
    Miszczak, Jaroslaw A.
    NEW JOURNAL OF PHYSICS, 2024, 26 (01):
  • [33] Deep Learning Techniques for EEG Signal Applications - a Review
    Praveena, D. Merlin
    Sarah, D. Angelin
    George, S. Thomas
    IETE JOURNAL OF RESEARCH, 2022, 68 (04) : 3030 - 3037
  • [34] MOBILE ROBOT NAVIGATION IN HILLY TERRAIN USING REINFORCEMENT LEARNING TECHNIQUES
    Tennety, Srinivas
    Kumar, Manish
    PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE AND BATH/ASME SYMPOSIUM ON FLUID POWER AND MOTION CONTROL (DSCC 2011), VOL 2, 2012, : 81 - 86
  • [35] Q-RAN:: A constructive reinforcement learning approach for robot behavior learning
    Jun, Li
    Lilienthal, Achim
    Martinez-Martin, Tomas
    Duckett, Tom
    2006 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-12, 2006, : 2656 - 2662
  • [36] A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade
    Khan, Md. Al-Masrur
    Khan, Md Rashed Jaowad
    Tooshil, Abul
    Sikder, Niloy
    Mahmud, M. A. Parvez
    Kouzani, Abbas Z.
    Abdullah-Al Nahid
    IEEE ACCESS, 2020, 8 : 176598 - 176623
  • [37] Reinforcement learning for robot research: A comprehensive review and open issues
    Zhang, Tengteng
    Mo, Hongwei
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2021, 18 (03)
  • [38] Deep Reinforcement Learning Based Mobile Robot Navigation: A Review
    Zhu, Kai
    Zhang, Tao
    TSINGHUA SCIENCE AND TECHNOLOGY, 2021, 26 (05) : 674 - 691
  • [40] Deep Reinforcement Learning Based Mobile Robot Navigation:A Review
    Kai Zhu
    Tao Zhang
    Tsinghua Science and Technology, 2021, 26 (05) : 674 - 691