Passive Task-Prioritized Shared-Control Teleoperation with Haptic Guidance

Selvaggio, Mario; Giordano, Paolo Robuffo; Ficuciello, Fanny; Siciliano, Bruno

doi:10.1109/icra.2019.8794197

Cited by 38 publications

(34 citation statements)

References 35 publications

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“…The angle α i can be easily calculated given f c,i as α i = arccos ẑ T i f ci / f ci ≥ 0. The function H (α) is, thus, designed as [11]…”

Section: A Object Non-sliding Controllermentioning

confidence: 99%

“…Examples range from grasping and manipulating objects using remote manipulator arms [7] (possibly accounting for post-grasping objectives [8]), to collaborative transportation of large objects using a team of mobile robots [9], [10]. Employing shared-control techniques is particularly useful when dealing with complex tasks and/or many degree-of-freedom (DoF) robotic systems [11], as direct control would result in cumbersome, time-consuming, and cognitively-demanding task execution.…”

Section: Introductionmentioning

confidence: 99%

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A Shared-Control Teleoperation Architecture for Nonprehensile Object Transportation

et al. 2022

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This article proposes a shared-control teleoperation architecture for robot manipulators transporting an object on a tray. Differently from many existing studies about remotely operated robots with firm grasping capabilities, we consider the case in which, in principle, the object can break its contact with the robot end-effector. The proposed shared-control approach automatically regulates the remote robot motion commanded by the user and the end-effector orientation to prevent the object from sliding over the tray. Furthermore, the human operator is provided with haptic cues informing about the discrepancy between the commanded and executed robot motion, which assist the operator throughout the task execution.We carried out trajectory tracking experiments employing an autonomous 7 degree-of-freedom (DoF) manipulator and compared the results obtained using the proposed approach with two different control schemes (i.e., constant tray orientation and no motion adjustment). We also carried out a human-subjects study involving eighteen participants, in which a 3-DoF haptic device was used to teleoperate the robot linear motion and display haptic cues to the operator. In all experiments, the results clearly show that our control approach outperforms the other solutions in terms of sliding prevention, robustness, commands tracking, and user's preference.

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“…The angle α i can be easily calculated given f c,i as α i = arccos ẑ T i f ci / f ci ≥ 0. The function H (α) is, thus, designed as [11]…”

Section: A Object Non-sliding Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Shared-Control Teleoperation Architecture for Nonprehensile Object Transportation

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In ref. [111], a passive task-prioritised shared-control method for remote telemanipulation of redundant robots was proposed. Haptic feedback and guidance have been shown to play a significant and promising role in shared-control applications.…”

Section: Teleoperation Controlmentioning

confidence: 99%

A review on manipulation skill acquisition through teleoperation‐based learning from demonstration

Wang

Yang

2021

Cognitive Comp and Systems

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Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realise the manipulation skill learning and generalisation. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalisation to deal with complex manipulation tasks. To this end, the key technologies, for example, manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Yet, the interaction between cooperative robot teams and human operators may become unstable [2]. Being sufficient for stabilizing robot teams in interaction with passive users, the passivity of the robot team has become a prevalent design objective in teleoperation control [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Distributed Winner-Take-All Teleoperation of A Multi-Robot System

Yuan

Constantinescu

Shi

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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In a distributed multi-master-multi-slave teleoperation system, the human users may compete against each other for the control of the team of slave robots. To win the competition, one operator would send the largest command to the slave group. For the sake of team cohesion, the slave group should follow the command of the winning operator and ignore the commands of the other users. To enable (i) the slave team to identify the winning operator, and (ii) each slave to determine whether to admit or discard the command it receives from its operator, this paper proposes a dynamic decision-making protocol that distinguishes the decision variable of the slave commanded by the winner from the decision variables of all other slave robots. The protocol only requires the slaves to exchange and evaluate their decision variables locally. Lyapunov stability analysis proves the theoretical convergence of the proposed decision-making algorithm. An experimental distributed winner-take-all teleoperation in a 3-masters-11-slaves teleoperation testbed validates its practical efficacy.

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Passive Task-Prioritized Shared-Control Teleoperation with Haptic Guidance

Cited by 38 publications

References 35 publications

A Shared-Control Teleoperation Architecture for Nonprehensile Object Transportation

A Shared-Control Teleoperation Architecture for Nonprehensile Object Transportation

A review on manipulation skill acquisition through teleoperation‐based learning from demonstration

Distributed Winner-Take-All Teleoperation of A Multi-Robot System

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