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
  • [21] Direct gradient-based reinforcement learning for robot behavior learning
    El-Fakdi, Andres
    Carreras, Marc
    Ridao, Pere
    INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS II, 2007, : 175 - +
  • [22] Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques
    Pintos Gomez de las Heras, Borja
    Martinez-Tomas, Rafael
    Cuadra Troncoso, Jose Manuel
    APPLIED SCIENCES-BASEL, 2024, 14 (01):
  • [23] Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review
    Al-Hamadani, Mokhaled N. A.
    Fadhel, Mohammed A.
    Alzubaidi, Laith
    Balazs, Harangi
    SENSORS, 2024, 24 (08)
  • [24] A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Collaborative Robot
    Sajwan, Mohit
    Singh, Simranjit
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2023, 30 (06) : 3489 - 3508
  • [25] A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Collaborative Robot
    Mohit Sajwan
    Simranjit Singh
    Archives of Computational Methods in Engineering, 2023, 30 : 3489 - 3508
  • [26] Reinforcement learning of walking behavior for a four-legged robot
    Kimura, H
    Yamashita, T
    Kobayashi, S
    PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 2001, : 411 - 416
  • [27] A Multiobjective Collaborative Deep Reinforcement Learning Algorithm for Jumping Optimization of Bipedal Robot
    Tao, Chongben
    Li, Mengru
    Cao, Feng
    Gao, Zhen
    Zhang, Zufeng
    ADVANCED INTELLIGENT SYSTEMS, 2024, 6 (01)
  • [28] Digital twin for autonomous collaborative robot by using synthetic data and reinforcement learning
    Kim, Dongjun
    Choi, Minho
    Um, Jumyung
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2024, 85
  • [29] Behavior coordination for a mobile robot using modular reinforcement learning
    Uchibe, E
    Asada, M
    Hosoda, K
    IROS 96 - PROCEEDINGS OF THE 1996 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS - ROBOTIC INTELLIGENCE INTERACTING WITH DYNAMIC WORLDS, VOLS 1-3, 1996, : 1329 - 1336
  • [30] Behavior parameters' optimization of robot soccer based on reinforcement learning
    Gu, D.L.
    Chen, W.D.
    Xi, Y.G.
    Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2001, 14 (02):