Massage manipulator training method based on deep reinforcement learning

Abstract

The invention discloses a massage manipulator training method based on deep reinforcement learning. The problems that in the prior art, the action of a massage manipulator is only in a simulation state, and the massage action is not accurate enough are solved. The massage manipulator training method has the effects of enhancing the skill of the massage manipulator, providing professional and accurate massage, and reducing the fatigue of manual massage. The massage manipulator training method comprises the following steps that actions and pressure data are acquired, the data are processed, a reference action set and a reference pressure set are constructed, and a pressure value comfort range is set; the data, the reference action and the reference pressure are input into a neural network tobe predicted and decided, an action value and a pressure value corresponding to the output decision of the neural network are executed, and the action value and the pressure value are compared with the reference action and the pressure value comfort range; and the trained network is connected with a control system of the massage manipulator after the set condition is met.

Description

technical field [0001] The invention relates to the field of manipulators, in particular to a massage manipulator training method based on deep reinforcement learning. Background technique [0002] Nowadays, the mechanical equipment used for massage is not diversified. Most of them are single-function or multi-functional massagers, massage chairs, etc., with few movements and mechanization. It is difficult to grasp the use of force, and cannot provide users with more comfortable, More professional service. Manual massage movements are delicate and soft, especially professional massage, which is highly skilled and skillful. However, the number of professional masseuses is small, and they cannot provide services anytime and anywhere, and the cost is relatively high, so they cannot meet the needs of ordinary people. [0003] With the development of artificial intelligence and the continuous improvement of productivity requirements, industrial robotic arms have been used in mo...

AI Technical Summary

Problems solved by technology

Due to the high-dimensional sample complexity and other physical limitations of the reinforcement learning algorithm, the combination of deep learning and reinforcement learning greatly reduces the dimension and complexity of the data, but it is currently only in the simulation state and cannot be fully applied to the actual

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