Learning Parameters of Damping Field using Bayesian Optimization for Human-robot Cooperation

Human-Robot Collaboration (HRC) systems are becoming widespread in industrial applications owing to the advantages of enhancing work productivity and reducing production expenses. In this study, we focus on HRC systems that involve physical interactions between humans and robots. The relationship between force and position at the robot's end-effector is generally modulated using impedance or admittance control techniques to implement these systems. Furthermore, varying the target impedance of robots has been shown to enhance their performance in HRC tasks. This report presents a novel approach to admittance learning strategy aimed at minimizing human effort during physical human-robot collaboration tasks. A damping generation scheme based on Gaussian basis functions is introduced, enabling the generation of a diverse range of smooth damping profiles via the modulation of the weights of these functions. The Gaussian design is based on a frequency analysis of human movement, with weights adjusted via gradient descent to minimize the interaction force. A learning algorithm based on the generalized simplex gradient approximation technique is proposed to accommodate the noisy evaluation function, utilizing data from previous iterations to enhance estimation accuracy. The effectiveness of the proposed method is experimentally demonstrated through comparison to conventional methods, as well as trials involving a complex task.

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Admittance learning strategy using generalized simplex gradient methods for human–robot collaboration

Tran

Yamawaki

FUJIWARA

et al. 2023

Mechanical Engineering Journal

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Variable admittance control based on iterative learning scheme with adaptive gradient methods for human-robot collaborative manipulation

Yamawaki

Liem

IZUMI

et al. 2023

Transactions of the JSME (In Japanese)

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Human-robot cooperation systems can combine the skills of humans and the power of robots, which can improve productivity and flexibility and reduce the physical burden on workers. Admittance control has often been applied to the collaborative task with physical human-robot interactions. However, in the conventional methods, the admittance parameter was adjusted based on heuristic methods. The authors have proposed an iterative learning control scheme that can update admittance parameters to reduce the physical burden on the operator in the collaborative task. However, there was a problem that the learning performance was significantly influenced by uncertain data such as noise and outliers because the steepest descent method, which has a fixed learning rate, is employed in the updating law. Furthermore, the manual learning-rate adjustment by trial and error was required to improve learning performance. In recent years, research on adaptive gradient methods that vary the learning rate has been actively conducted in the fields of machine learning, aiming at improving learning performance. In this paper, we propose a novel iterative learning control scheme with adaptive gradient methods for human-robot collaborative manipulation to improve the learning performance against uncertain data and lower the cost of adjusting the learning rate. The validity of the proposed method is demonstrated through extensive experiments, including 1) cooperative operations in the presence of obstacles and 2) cooperative transport of heavy objects.

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Learning Parameters of Damping Field using Bayesian Optimization for Human-robot Cooperation

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Admittance learning strategy using generalized simplex gradient methods for human–robot collaboration

Admittance learning strategy using generalized simplex gradient methods for human–robot collaboration

Variable admittance control based on iterative learning scheme with adaptive gradient methods for human-robot collaborative manipulation

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