Robot Variable Impedance Skill Transfer and Learning Framework Based on a Simplified Human Arm Impedance Model

Wu, Rui; Zhang, He; Zhao, Jie

doi:10.1109/access.2020.3043524

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…represents the length of the three semiaxes (X, Y, Z direction in Cartesian space) of the stiffness ellipsoid in human-arm impedance that is estimated by human-arm posture and EMG signals [38]. Fig.…”

Section: Learning Io Model and Va Strategymentioning

confidence: 99%

“…The EMG data of flexor carpiulnaris on human forearm is recorded by TI ADS1298 bio-signal amplifiers (1000 Hz). To obtain the estimation of human-arm impedance, we apply a similar method, as described in [38]. The arm position and EMG signal, we collected are processed as the input of a conversion function; and the output of this function is the arm end-point impedance.…”

Section: A Record and Analysis Of Human Cutting-datamentioning

confidence: 99%

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Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

Billard

2022

IEEE/ASME Trans. Mechatron.

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In this article, we propose an approach to extract variable-impedance during cutting tasks from human demonstrations, so as to ease soft-tissue cutting by robots. We model the dynamic adjustment of the human arm during interactions with the tissue and transfer these adaptive capabilities to the robot, by learning both the motion and change of impedance. To improve performance during task execution, our variable-impedance skill-transfer framework combines the learned model with an interactive-operation and feedback controller. To offer the flexibility of modifying the trajectory at run time, we use a control law based on dynamical systems. We couple this control law with virtual dynamics that describes the cutting dynamics. This ensures that the robot can control the interaction force and can plan the trajectory. The approach is validated in a real robot cutting-experiment of cutting different hardness tissues. Results show that our learning-based feedback controller, with the assistance from interactive-operation models, can effectively improve the task's success rate, cutting length, cutting depth, and other indicators, as compared to using a fixed and variable-impedance gain controller.

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Section: Learning Io Model and Va Strategymentioning

confidence: 99%

Section: A Record and Analysis Of Human Cutting-datamentioning

confidence: 99%

Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

Billard

2022

IEEE/ASME Trans. Mechatron.

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Impedance adjustment for learning human variable impedance skill

Wu,

Zhang,

Zhu

et al. 2023

Mechatronics

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Sensorless variable impedance compliant control for human–robot interaction

Liu

Wang

2022

AIP Advances

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In order to improve compliance in human–robot interaction, a sensorless variable impedance control method is proposed. Variable impedance control is constructed using the end-effector velocity and the human–robot interaction force. Then, the impedance parameters are adjusted online to make the end-effector velocity more compliant with human behavior. Next, a velocity controller is designed to stabilize the end-effector velocity, and a compensation force is designed to avoid the measurement of the interaction force. Based on the velocity controller and the compensation force, an integrated controller is constructed to achieve stable end-effector velocity while compensating the interaction force online. The Lyapunov theory proves that with the sensorless variable impedance control, the system is stable, and the velocity tracking is bounded; simulation results demonstrate its feasibility. The sensorless variable impedance control can achieve more stable velocity and better compliance in human–robot interaction.

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Robot Variable Impedance Skill Transfer and Learning Framework Based on a Simplified Human Arm Impedance Model

Cited by 7 publications

References 35 publications

Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

Impedance adjustment for learning human variable impedance skill

Sensorless variable impedance compliant control for human–robot interaction

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