Dynamic strategy selection for physical robotic assistance in partially known tasks

Medina, Josep R.; Lawitzky, Martin; Molin, Adam; Hirche, Sandra

doi:10.1109/icra.2013.6630721

Cited by 24 publications

(21 citation statements)

References 16 publications

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“…In fully collaborative control, the robot is made to be aware of its environment and play both adaptive and selfreliance roles hence, making the collaboration between human and robot intuitive. A lot of efforts have been made to address the interactive behaviour between human and robot as demonstrated in [153]. In [154], the authors proposed a robot adaptive control focusing on allowing the robot to play the roles of either a task leader or follower based on the intention of human collaborator.…”

Section: B Control Designs In Phrcmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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Section: B Control Designs In Phrcmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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“…While how to address these two issues individually is still an open problem, a general framework is required to take both of them into account simultaneously. Therefore, adaptive frameworks/models for human-robot interaction have been proposed in recent studies [11], [7], [12], beyond a simple yet robust passive leader-follower model [13]. These studies point out that the robot should play an adaptive role to lead a task or to follow based on the human's intention or a specific hit.li.yn@gmail.com circumstance, where the role is usually relevant to the balance of contributions of the human and the robot in a task [14].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration

Tee

Yan

et al. 2016

IEEE Trans. Robot.

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Abstract-In this paper, we propose a framework to analyze the interactive behaviors of human and robot in physical interactions. Game theory is employed to describe the system under study, and policy iteration is adopted to provide a solution of Nash equilibrium. The human's control objective is estimated based on the measured interaction force, and it is used to adapt the robot's objective such that human-robot coordination can be achieved. The validity of the proposed method is verified through a rigorous proof and experimental studies.

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“…It required that a shared plan in the form of a desired trajectory, as well as the common goal of the cooperation task, be known to both agents. In [23], the possibility that the human diverges from the robot's assumed final configuration or path to the goal was considered, and an adaptation strategy was developed to switch between model-based and model-free predictions based on risk-sensitive optimal feedback control [24]. In [25], role adaptation was achieved by adaptive attitude design depending on the disagreement level and the environmental situation, and the possibility of using game theory for the modeling of attitude negotiation between two partners was suggested.…”

Section: Introductionmentioning

confidence: 99%

Continuous Role Adaptation for Human–Robot Shared Control

et al. 2015

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Abstract-In this paper, we propose a role adaptation method for human-robot shared control. Game theory is employed for fundamental analysis of this two-agent system. An adaptation law is developed such that the robot is able to adjust its own role according to the human's intention to lead or follow, which is inferred through the measured interaction force. In the absence of human interaction forces, the adaptive scheme allows the robot to take the lead and complete the task by itself. On the other hand, when the human persistently exerts strong forces that signal an unambiguous intent to lead, the robot yields and becomes the follower. Additionally, the full spectrum of mixed roles between these extreme scenarios is afforded by continuous online update of the control that is shared between both agents. Theoretical analysis shows that the resulting shared control is optimal with respect to a two-agent coordination game. Experimental results illustrate better overall performance, in terms of both error and effort, compared to fixed-role interactions.

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Dynamic strategy selection for physical robotic assistance in partially known tasks

Cited by 24 publications

References 16 publications

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration

Continuous Role Adaptation for Human–Robot Shared Control

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