IEEE, pp 1802–1807Ĭhen X, Wang N, Cheng H, Yang C (2020) Neural learning enhanced variable admittance control for human-robot collaboration. In: 2020 15th IEEE conference on industrial electronics and applications (ICIEA). Product Eng 1–26Ĭhen X, Jiang Y, Yang C (2020) Stiffness estimation and intention detection for human-robot collaboration. Gervasi R, Mastrogiacomo L, Franceschini F (2023) An experimental focus on learning effect and interaction quality in human–robot collaboration. Mukherjee D, Gupta K, Chang LH, Najjaran H (2022) A survey of robot learning strategies for human-robot collaboration in industrial settings. Int J Robot Res 36(5–7):580–596īauer A, Wollherr D, Buss M (2008) Human-robot collaboration: a survey. Hiatt LM, Narber C, Bekele E, Khemlani SS, Trafton JG (2017) Human modeling for human-robot collaboration. Ogenyi UE, Liu J, Yang C, Ju Z, Liu H (2019) Physical human-robot collaboration: robotic systems, learning methods, collaborative strategies, sensors, and actuators. ![]() Saito N, Ogata T, Funabashi S, Mori H, Sugano S (2021) How to select and use tools?: active perception of target objects using multimodal deep learning. J Robot Surg 6(1):53–63Īmarillo A, Sanchez E, Caceres J, Oñativia J (2021) Collaborative human-robot interaction interface: development for a spinal surgery robotic assistant. Jacob M, Li Y-T, Akingba G, Wachs JP (2012) Gestonurse: a robotic surgical nurse for handling surgical instruments in the operating room. Molitor M, Renkema M (2022) Human-robot collaboration in a smart industry context: does hrm matter? In: Smart Industry–Better Management, vol 28. Matheson E, Minto R, Zampieri EG, Faccio M, Rosati G (2019) Human-robot collaboration in manufacturing applications: a review. the mediating role of work engagement: a survey. Paliga M (2022) Human-cobot interaction fluency and cobot operators’ job performance. When the key characteristics of these works are compared, patterns begin to emerge with the cobot and provide directions for future efforts, contrasting them with other parts of the cobot that have not been reviewed.īloss R (2016) Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications. In addition to these realizations, the significance of machine learning techniques is emphasized-to account for temporal factors. After that, a comprehensive evaluation examines the features of several classes of machine learning, deep learning algorithms, and the sensing techniques employed it is carried out. It proposes a paradigm of cognitive characteristics, evaluation measures, and collaborative tasks. A large number of publications were chosen for this analysis, and a grouping of works was performed based on the type. As a result, this study’s purpose suggests a comprehensive literature analysis on machine learning’s use of humans– Putting together robots to work together. It’s only been relatively recently that we’ve begun to use machine learning to provide a cognitive framework and behavioral building block for high-quality human resource management. ![]() In the canon of cobot’s writings, typically, one constructs a cognitive model that, among other things, takes in data about the users and the world and transforms them into data that the robot can use. Collaborative robot (cobot) is a methodology that investigates the cognitive and physical interaction between humans and robots as they work together to achieve a common goal. More and more, we may expect to see robots working side by side with humans as technology advances.
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