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 | 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},
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 | 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},
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. |
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},
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. |
