Collaborative Capabilities in Physical Human-Robot Interaction Scenarios
The aim of PhRoCiety is to advance cognitive understanding and the current control about cooperative and robust multi-contact physical interaction between multiple agents, where agents are humans or robots. PhRoCiety will go beyond traditional approaches by: (1) proposing novel control methodologies for performing cooperative tasks with multiple agents in physical human-robot or robot-robot interaction scenarios; (2) combining cognitive learning and physical interaction with predictable and unpredictable events; (3) validating theoretical advances in real-world physical interactive scenarios.
Firstly, PhRoCiety will advance state-of-the-art in the way robots learn and perform cooperative tasks, by developing new tools for learning predictive models of human dynamic behavior in collaborative scenarios. Here the aim is to study neuromechanical parameters of human-human collaborative behaviors.
Secondly, PhRoCiety will propose a novel control framework that will innovate the robotic technology for assisting humans performing physical collaborative tasks. By measuring and modeling human dynamics in human-human collaborative setups, PhRoCiety will advance the state-of-the art in autonomous robot companions that interact with humans to provide assistance, work alongside with humans as peers, learn from humans as apprentices, and foster more engaging physical interaction between them.
Thirdly, another achievement of PhRoCiety will be the validation of methods in real-world scenarios. The evaluations will show robots exploiting cooperative partnership with humans for cognitive learning and utilizing assistive physical interaction.
Combining Virtual and Physical Guides for Autonomous In-Contact Path Adaptation Inproceedings
Zeghloul, Said; Laribi, Med Amine; Arevalo, Juan Sebastian Sandoval (Ed.): Advances in Service and Industrial Robotics, pp. 181–189, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-48989-2.
Dyadic Human-Human Interactions in Reaching Tasks: Fitts' Law for Two Inproceedings
Zeghloul, Said; Laribi, Med Amine; Arevalo, Juan Sebastian Sandoval (Ed.): Advances in Service and Industrial Robotics, pp. 199–207, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-48989-2.
Frontiers in Neurorobotics, 14 , pp. 90, 2020, ISSN: 1662-5218.
ARRS grant no.: N2-0130