Study on force control for robot massage with a model-based reinforcement learning algorithm

Xiao, Meng; Zhang, Tie; Zou, Yanbiao; Yan, Xiaohu; Wu, Wen

doi:10.1007/s11370-023-00474-6

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Zhai et al (2022) integrated graph-based knowledge transfer learning into particle swarm optimization identification algorithm, designing an adaptive impedance control algorithm to ensure accurate position and force control of massage robot. Xiao et al (2023) combined neural networks with the cross-entropy method to construct a reinforcement learning algorithm for impedance control parameter search. In a contact scenario with the human body, the biomechanical characteristics of the skin tissue can vary considerably with the site of contact.…”

Section: Introductionmentioning

confidence: 99%

“…Massage is a challenging task; the size of different people varies greatly, and the shape and curvature of surfaces also varies greatly from different massage areas in the same person. Skin materials vary with age, skin quality, obesity and other factors of different individuals, and even for the same individual, different skin regions will show different mechanical characteristics due to the different distribution ratios of bone, muscle and fat (Quek et al , 2019; Xiao et al , 2023). Thus, it needs to accommodate motion planning for a variety of human surfaces, as well as safe and compliant interactions when the robot is in direct contact with humans.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Zhang,

Xiong,

Yin

et al. 2024

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Purpose To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control. Design/methodology/approach First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online. Findings Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment. Originality/value This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Zhang,

Xiong,

Yin

et al. 2024

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Compliance control of a rehabilitation massage robot in dynamic scenes

Wang,

Chen,

et al. 2024

J. Phys.: Conf. Ser.

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Medical robots encounter challenges when interacting with people or operating in complex and dynamic environments due to the variability of human morphology and the unpredictability of environmental changes. Compliance of human-robot interaction is the primary goal of medical robots when in contact with the human body. Therefore, robots must be able to adaptively adjust their forces and actions to ensure safety and comfort during the contact process. This paper focuses on the compliance control of rehabilitation massage robots in dynamic scenes. We propose a mechanical arm compliance control method based on the Soft Actor-Critic (SAC) algorithm. We construct a simulated massage environment in a dynamic scene according to the task requirements and design a massage path covering the entire back. Under the framework of deep reinforcement learning, the optimal reward function is designed to achieve constant force control under dynamic scenes. Through numerous simulation experiments, we have verified that the robotic arm can move along the predetermined path under the massage while maintaining a constant contact force with the body simulation module. The actual contact force and target contact force control are realized within 0.1 N.

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Research on Robot Massage Force Control Based on Residual Reinforcement Learning

Xiao,

Zhang,

Zou

et al. 2024

IEEE Access

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To address the problem that traditional force control methods have difficulty obtaining stable forces during a robot massage, a robot force control algorithm based on residual reinforcement learning is proposed. An initial strategy of the robot massage force is first constructed with impedance control, but the massage force often fluctuates when the skin environment is unknown to the robot. A reinforcement learning algorithm is then used to analyze the relationship between the robot contact state and offset displacement and compensate for the residuals of the impedance controller. To speed up the search for the compensation strategy, a neural network is constructed to fit a dynamic model of reinforcement learning with the data from the initial strategy, which can simulate the contact between the robot and skin. Offline training in a simulated environment can reduce the number of actual interactions in reinforcement learning and improve the practicability of the algorithm; to integrate the two algorithms, the output of the residual reinforcement learning strategy is smoothed. The experimental results show that the robot force control algorithm based on residual reinforcement learning converges after approximately 80 offline iterations. The force error in the online experiment is basically within ±0.2 N.

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Study on force control for robot massage with a model-based reinforcement learning algorithm

Cited by 4 publications

References 32 publications

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Compliance control of a rehabilitation massage robot in dynamic scenes

Research on Robot Massage Force Control Based on Residual Reinforcement Learning

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