PhRoCiety

@article{leskovar2022leader,

title = {Leader-Follower Dynamics in Complex Obstacle Avoidance Task},

author = {Rebeka Kropivšek Leskovar and Jernej Čamernik and Tadej Petrič},

url = {https://arxiv.org/abs/2207.04791},

doi = {https://doi.org/10.48550/arXiv.2207.04791},

year  = {2022},

date = {2022-01-01},

journal = {arXiv preprint arXiv:2207.04791},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}