Journal Articles
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 | Leskovar, Rebeka Kropivšek; Čamernik, Jernej; Petrič, Tadej Leader–Follower Role Allocation for Physical Collaboration in Human Dyads Journal Article Applied Sciences, 11 (19), 2021, ISSN: 2076-3417. Abstract | Links | BibTeX @article{Leskovar2021,
title = {Leader–Follower Role Allocation for Physical Collaboration in Human Dyads},
author = {Rebeka Kropivšek Leskovar and Jernej Čamernik and Tadej Petrič},
url = {https://www.mdpi.com/2076-3417/11/19/8928},
doi = {10.3390/app11198928},
issn = {2076-3417},
year = {2021},
date = {2021-09-25},
journal = {Applied Sciences},
volume = {11},
number = {19},
abstract = {People often find themselves in situations where collaboration with others is necessary to accomplish a particular task. In such cases, a leader–follower relationship is established to coordinate a plan to achieve a common goal. This is usually accomplished through verbal communication. However, what happens when verbal communication is not possible? In this study, we observe the dynamics of a leader–follower relationship in human dyads during collaborative tasks where there is no verbal communication between partners. Using two robotic arms, we designed a collaborative experimental task in which subjects perform the task individually or coupled together through a virtual model. The results show that human partners fall into the leader–follower dynamics even when they cannot communicate verbally. We demonstrate this in two steps. First, we study how each subject in a collaboration influences task performance, and second, we evaluate whether both partners influence it equally or not using our proposed sorting method to objectively identify a leader. We also study the leader–follower dynamics by analysing the task performance of partners during their individual sessions to predict the role distribution in a dyad. Based on the results of our prediction method, we conclude that the higher-performing individual performance will assume the role of a leader in collaboration.},
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pubstate = {published},
tppubtype = {article}
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People often find themselves in situations where collaboration with others is necessary to accomplish a particular task. In such cases, a leader–follower relationship is established to coordinate a plan to achieve a common goal. This is usually accomplished through verbal communication. However, what happens when verbal communication is not possible? In this study, we observe the dynamics of a leader–follower relationship in human dyads during collaborative tasks where there is no verbal communication between partners. Using two robotic arms, we designed a collaborative experimental task in which subjects perform the task individually or coupled together through a virtual model. The results show that human partners fall into the leader–follower dynamics even when they cannot communicate verbally. We demonstrate this in two steps. First, we study how each subject in a collaboration influences task performance, and second, we evaluate whether both partners influence it equally or not using our proposed sorting method to objectively identify a leader. We also study the leader–follower dynamics by analysing the task performance of partners during their individual sessions to predict the role distribution in a dyad. Based on the results of our prediction method, we conclude that the higher-performing individual performance will assume the role of a leader in collaboration. |
 | Knezevic, Nikola; Lukic, Branko; Jovanovic, Kosta; Zlajpah, Leon; Petric, Tadej End-effector Cartesian stiffness shaping - sequential least squares programming approach Journal Article Serbian Journal of Electrical Engineering, 18 (1), pp. 1–14, 2021, ISSN: 1451-4869. Abstract | Links | BibTeX @article{Knezevic2021,
title = {End-effector Cartesian stiffness shaping - sequential least squares programming approach},
author = {Nikola Knezevic and Branko Lukic and Kosta Jovanovic and Leon Zlajpah and Tadej Petric},
url = {http://www.doiserbia.nb.rs/Article.aspx?ID=1451-48692101001K
http://cobotat.ijs.si/wp-content/uploads/2021/04/Knezevic-et-al._2021_End-effector-Cartesian-stiffness-shaping-sequential-least-squares-programming-approach_Serbian-Journal-of-Electri.pdf},
doi = {10.2298/SJEE2101001K},
issn = {1451-4869},
year = {2021},
date = {2021-01-01},
journal = {Serbian Journal of Electrical Engineering},
volume = {18},
number = {1},
pages = {1--14},
abstract = {Control of robot end-effector (EE) Cartesian stiffness matrix (or the whole mechanical impedance) is still a challenging open issue in physical humanrobot interaction (pHRI). This paper presents an optimization approach for shaping the robot EE Cartesian stiffness. This research targets collaborative robots with intrinsic compliance – serial elastic actuators (SEAs). Although robots with SEAs have constant joint stiffness, task redundancy (null-space) for a specific task could be used for robot reconfiguration and shaping the stiffness matrix while still keeping the EE position unchanged. The method proposed in this paper to investigate null-space reconfiguration's influence on Cartesian robot stiffness is based on the Sequential Least Squares Programming (SLSQP) algorithm, which presents an expansion of the quadratic programming algorithm for nonlinear functions with constraints. The method is tested in simulations for 4 DOF planar robot. Results are presented for control of the EE Cartesian stiffness initially along one axis, and then control of stiffness along both planar axis – shaping the main diagonal of the EE stiffness matrix.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Control of robot end-effector (EE) Cartesian stiffness matrix (or the whole mechanical impedance) is still a challenging open issue in physical humanrobot interaction (pHRI). This paper presents an optimization approach for shaping the robot EE Cartesian stiffness. This research targets collaborative robots with intrinsic compliance – serial elastic actuators (SEAs). Although robots with SEAs have constant joint stiffness, task redundancy (null-space) for a specific task could be used for robot reconfiguration and shaping the stiffness matrix while still keeping the EE position unchanged. The method proposed in this paper to investigate null-space reconfiguration's influence on Cartesian robot stiffness is based on the Sequential Least Squares Programming (SLSQP) algorithm, which presents an expansion of the quadratic programming algorithm for nonlinear functions with constraints. The method is tested in simulations for 4 DOF planar robot. Results are presented for control of the EE Cartesian stiffness initially along one axis, and then control of stiffness along both planar axis – shaping the main diagonal of the EE stiffness matrix. |
 | Petrič, Tadej Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs) Journal Article Frontiers in Neurorobotics, 14 , pp. 90, 2020, ISSN: 1662-5218. Abstract | Links | BibTeX @article{10.3389/fnbot.2020.599889,
title = {Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)},
author = {Tadej Petrič},
url = {https://www.frontiersin.org/article/10.3389/fnbot.2020.599889
http://cobotat.ijs.si/wp-content/uploads/2021/04/Petric_2020_Phase-Synchronized-Learning-of-Periodic-Compliant-Movement-Primitives-P-CMPs_Frontiers-in-Neurorobotics.pdf},
doi = {10.3389/fnbot.2020.599889},
issn = {1662-5218},
year = {2020},
date = {2020-01-01},
journal = {Frontiers in Neurorobotics},
volume = {14},
pages = {90},
abstract = {Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios. |
Inproceedings
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 | Leskovar, Rebeka Kropivšek; Petrič, Tadej Humans Prefer Collaborating with a Robot Who Leads in a Physical Human-Robot Collaboration Scenario Inproceedings 2021 20th International Conference on Advanced Robotics (ICAR), pp. 935-941, 2021. Links | BibTeX @inproceedings{9659365,
title = {Humans Prefer Collaborating with a Robot Who Leads in a Physical Human-Robot Collaboration Scenario},
author = {Rebeka Kropivšek Leskovar and Tadej Petrič},
doi = {10.1109/ICAR53236.2021.9659365},
year = {2021},
date = {2021-01-01},
booktitle = {2021 20th International Conference on Advanced Robotics (ICAR)},
pages = {935-941},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
 | Rebeka, Kropivšek Leskovar ; Petrič, Tadej Turing test of motor ability perception in physical collaboration between a human and an intelligent robot agent Inproceedings Žemva Andrej, Trost Andrej (Ed.): Proceedings of the Twenty-ninth International Electrotechnical and Computer Science Conference ERK 2020, 2020, ISBN: 2591-0442, 29. Abstract | Links | BibTeX @inproceedings{kropivsek2020,
title = {Turing test of motor ability perception in physical collaboration between a human and an intelligent robot agent},
author = {Rebeka, Kropivšek Leskovar and Tadej Petrič},
editor = {Žemva, Andrej, Trost, Andrej},
url = {http://cobotat.ijs.si/wp-content/uploads/2021/04/ERK2020.pdf},
isbn = {2591-0442, 29},
year = {2020},
date = {2020-09-21},
booktitle = {Proceedings of the Twenty-ninth International Electrotechnical and Computer Science Conference ERK 2020},
volume = {ERK 2020},
abstract = {In this paper we propose a novel robot control method for human-robot collaboration tasks that takes into account the leader-follower relationship found in human interactions. Taking into account the leader-follower dynamics, learnt during a study on human-human collaboration, the control method replicates human behaviour when performing collaborative tasks. The performance of the proposed control method was evaluated using a 2D reaching task where we compared task performance between individual tasks, tasks in collaboration with a human and tasks in collaboration with a robot. The subjects in the evaluation were asked to grade their perceived task load for each experiment as well as specify if they thought they performed the task alone, with a robot or with a human partner as a Turing test to determine whether the subjects were able to distinct between a robot and a human partner. The results of the evaluation showed, that the robot control method is capable of replicating human behavior to benefit overall task performance of the subject in collaboration, however it is not capable of replicating this behaviour to the degree that the subject in collaboration would not be able distinct whether they were collaborating with a robot or a human partner. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we propose a novel robot control method for human-robot collaboration tasks that takes into account the leader-follower relationship found in human interactions. Taking into account the leader-follower dynamics, learnt during a study on human-human collaboration, the control method replicates human behaviour when performing collaborative tasks. The performance of the proposed control method was evaluated using a 2D reaching task where we compared task performance between individual tasks, tasks in collaboration with a human and tasks in collaboration with a robot. The subjects in the evaluation were asked to grade their perceived task load for each experiment as well as specify if they thought they performed the task alone, with a robot or with a human partner as a Turing test to determine whether the subjects were able to distinct between a robot and a human partner. The results of the evaluation showed, that the robot control method is capable of replicating human behavior to benefit overall task performance of the subject in collaboration, however it is not capable of replicating this behaviour to the degree that the subject in collaboration would not be able distinct whether they were collaborating with a robot or a human partner. |
 | Petrič, Tadej; Žlajpah, Leon 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. Abstract | Links | BibTeX @inproceedings{10.1007/978-3-030-48989-2_20,
title = {Combining Virtual and Physical Guides for Autonomous In-Contact Path Adaptation},
author = {Tadej Petrič and Leon Žlajpah},
editor = {Said Zeghloul and Med Amine Laribi and Juan Sebastian Sandoval Arevalo},
url = {http://cobotat.ijs.si/wp-content/uploads/2021/04/Raad2020.pdf},
isbn = {978-3-030-48989-2},
year = {2020},
date = {2020-01-01},
booktitle = {Advances in Service and Industrial Robotics},
pages = {181--189},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Several approaches exist for learning and control of robot behaviors in physical human-robot interaction (PHRI) scenarios. One of these is the approach based on virtual guides which actively helps to guide the user. Such a system enables guiding users towards preferred movement directions or prevents them to enter into a prohibited zone. Despite being shown that such a framework works well in physical contact with humans, the efficient interaction with the environment is still limited. Within the virtual guide framework, the environment is considered as a physical guide, for example, a table is a plane that prevents the robot to penetrate through. To mitigate these limits we introduce and evaluate the means of autonomous path adaptation through interaction with physical guides, which essentially means merging virtual and physical guides. The virtual guide framework was extended by introducing an algorithm which partially modifies the virtual guides online. The path updates are now based on the interactive force measurements and essentially improves the virtual guides to match them with the actual physical guides.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Several approaches exist for learning and control of robot behaviors in physical human-robot interaction (PHRI) scenarios. One of these is the approach based on virtual guides which actively helps to guide the user. Such a system enables guiding users towards preferred movement directions or prevents them to enter into a prohibited zone. Despite being shown that such a framework works well in physical contact with humans, the efficient interaction with the environment is still limited. Within the virtual guide framework, the environment is considered as a physical guide, for example, a table is a plane that prevents the robot to penetrate through. To mitigate these limits we introduce and evaluate the means of autonomous path adaptation through interaction with physical guides, which essentially means merging virtual and physical guides. The virtual guide framework was extended by introducing an algorithm which partially modifies the virtual guides online. The path updates are now based on the interactive force measurements and essentially improves the virtual guides to match them with the actual physical guides. |
 | Leskovar, Rebeka Kropivšek; Čamernik, Jernej; Petrič, Tadej 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. Abstract | Links | BibTeX @inproceedings{10.1007/978-3-030-48989-2_22,
title = {Dyadic Human-Human Interactions in Reaching Tasks: Fitts' Law for Two},
author = {Rebeka Kropivšek Leskovar and Jernej Čamernik and Tadej Petrič},
editor = {Said Zeghloul and Med Amine Laribi and Juan Sebastian Sandoval Arevalo},
url = {http://cobotat.ijs.si/wp-content/uploads/2021/04/RAAD2020_Rkl_Final.pdf},
isbn = {978-3-030-48989-2},
year = {2020},
date = {2020-01-01},
booktitle = {Advances in Service and Industrial Robotics},
pages = {199--207},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {In this paper we examine physical collaboration between two individuals using a dual-arm robot as a haptic interface. First, we design a haptic controller based on a virtual dynamic model of the robot arms. Then, we analyse dyadic human-human collaboration with a reaching task on a 2D plane, where the distance and size of the target changed randomly from a pool of nine reachable positions and sizes. Each subject performed the task individually and linked through the guided robot arms with a virtual model to perform the same task in collaboration. We evaluated both, individual and collaborative performances, based on Fitts' law, which describes the relation between the speed of motion and its accuracy. The results show that the Fitts' law applies to both, individual and collaborative tasks, with their performance improving when in collaboration.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we examine physical collaboration between two individuals using a dual-arm robot as a haptic interface. First, we design a haptic controller based on a virtual dynamic model of the robot arms. Then, we analyse dyadic human-human collaboration with a reaching task on a 2D plane, where the distance and size of the target changed randomly from a pool of nine reachable positions and sizes. Each subject performed the task individually and linked through the guided robot arms with a virtual model to perform the same task in collaboration. We evaluated both, individual and collaborative performances, based on Fitts' law, which describes the relation between the speed of motion and its accuracy. The results show that the Fitts' law applies to both, individual and collaborative tasks, with their performance improving when in collaboration. |
