Reinforcement learning of variable admittance control for human-robot co-manipulation

Dimeas, Fotios; Aspragathos, Nikos Α.

doi:10.1109/iros.2015.7353494

Cited by 74 publications

(41 citation statements)

References 17 publications

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“…In this case, the tasks learned are to hold a table in a suitable position and cooperatively lift the table with human collaborator while keeping it horizontal. Dimeas et al in [142] applied reinforcement learning technique to learn variable admittance control for human-robot co-manipulation.…”

Section: Robot Learning Methodologiesmentioning

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: Robot Learning Methodologiesmentioning

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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“…Considering one side arm of the robot, the responds with the end-effector motion satisfying relationship as in (8). (9). The fourth order Runge-Kutta method is used to resolve differential equation and V ref can be obtained via differential method.…”

Section: Cartesian Admittance Controllermentioning

confidence: 99%

“…Their method can create a safe and compliant robot controller. Impedance and admittance control algorithms have been studied for human-robot cooperation task [7][8][9][10]. Dimeas et al [10] proposed a method of virtual Cartesian constraints to prevent the operator from guiding the manipulator to low-performance configurations.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Admittance Control for a Dual-Arm Robot under Space Limitation

et al. 2019

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Aimed at the situation lack of suitable industrial robots with speed requirement and space limitation, a novel simple structured and high speed dual-arm robot is designed. The robot control system has been achieved by using high speed controller, real-time bus EtherCAT and integrating the sensor system via Ethernet interface. Kinematic and dynamic analysis are the basis of its kinematic control and trajectory planning. This paper presents a force-free control method for direct teaching of the robot and adopts a Cartesian admittance control algorithm to realize human-machine interaction. The admittance control is conducted by utilizing six-dimensional force/torque sensor fixed to the end-effector of manipulator. To evaluate the performance of the proposed controller and control algorithm, a point-to-point teaching task is conducted., 0 (2019) MATEC Web of Conferences

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“…Kosuge [5] and Ikeura et al [6], [7] developed similar strategies for situations that required more flexibility, involving using direction of force and change in magnitude of force. These works sparked work in what is now known as variable-impedance control [8], [9]. This control method offers Cartesian control based on how much the magnitude of the force is changing, and in which direction the force is being applied on the follower.…”

Section: A State-of-the-art Controllersmentioning

confidence: 99%

Analysis of Rigid Extended Object Co-Manipulation by Human Dyads: Lateral Movement Characterization

Mielke¹,

Townsend²,

Killpack³

2017

Robotics: Science and Systems XIII

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Abstract-For co-manipulation involving humans and robots, robot controllers that are based on human-human behavior should allow more comfortable and coordinated movement between the human-robot dyad. In this paper, we describe an experiment between human-human dyads where we recorded the force and motion data as leader-follower dyads moved in translation and rotation. The force/motion data was then analyzed for patterns found during lateral translation only. For extended objects, lateral translation and in-place rotation are ambiguous, but this paper determines a way to characterize lateral translation triggers for future use in human-robot interaction. The study has 4 main results. First, interaction forces are non-negligible and are necessary for co-manipulation. Second, minimum-jerk trajectories are found in the lateral direction only for lateral movement. Third, the beginning of a lateral movement is characterized by distinct force triggers by the leader. Fourth, there are different metrics that can be calculated to determine which dyads moved most effectively in the lateral direction.

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Reinforcement learning of variable admittance control for human-robot co-manipulation

Cited by 74 publications

References 17 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

Design and Implementation of Admittance Control for a Dual-Arm Robot under Space Limitation

Analysis of Rigid Extended Object Co-Manipulation by Human Dyads: Lateral Movement Characterization

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