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Progress and prospects of the human–robot collaboration

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Abstract

Recent technological advances in hardware design of the robotic platforms enabled the implementation of various control modalities for improved interactions with humans and unstructured environments. An important application area for the integration of robots with such advanced interaction capabilities is human–robot collaboration. This aspect represents high socio-economic impacts and maintains the sense of purpose of the involved people, as the robots do not completely replace the humans from the work process. The research community’s recent surge of interest in this area has been devoted to the implementation of various methodologies to achieve intuitive and seamless human–robot-environment interactions by incorporating the collaborative partners’ superior capabilities, e.g. human’s cognitive and robot’s physical power generation capacity. In fact, the main purpose of this paper is to review the state-of-the-art on intermediate human–robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human–robot collaboration.

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Notes

  1. The problem of safety in human–robot interaction (HRI) and the related open issues have been extensively discussed in literature (Haddadin et al. 2009; De Santis et al. 2008; Alami et al. 2006). Hence, our focus in this review paper will be on other important aspects of physical human–robot collaboration (PHRC).

  2. A good overview of the cognitive aspects in HRC can be found in the literature, e.g., see Fong et al. (2003); Freedy et al. (2007); Rani et al. (2004).

  3. The effect of human sensorimotor learning is very crucial in such scenarios as the perception of the environment and the task by two experts will be different from two naive operators.

  4. The human effort can be identified in two stages: the physical and cognitive loads while learning a new collaborative task, and their amount on a regular basis after the human becomes an expert. This is important to note here that the CNS is capable of learning and adaptation to various tasks demands and disturbances, hence contributing to a reduction in the overall physical and cognitive loading while performing tasks with dynamic uncertainties (Burdet et al. 2001; Franklin et al. 2003). Nevertheless, the (robot) counterpart’s adaptive behaviour can affect such learning and adaptation processes in terms of time and performance.

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Acknowledgements

The authors would like to thank and remember Fabrizio Flacco for his spirit, contributions, and enthusiasm for writing this review paper. We will keep his memories alive in our hearts. This work is supported in part by the EU FP7-ICT projects WALKMAN (No. 611832) and CoDyCo (No. 600716); in part by the H2020 Projects SoftPro (No. 688857) and AnDy (No.731540).

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Correspondence to Arash Ajoudani.

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Learning for Human-Robot Collaboration.

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Ajoudani, A., Zanchettin, A.M., Ivaldi, S. et al. Progress and prospects of the human–robot collaboration. Auton Robot 42, 957–975 (2018). https://doi.org/10.1007/s10514-017-9677-2

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