Humanoid robot motion control method and system based on deep reinforcement learning

A robot motion, humanoid robot technology, applied in the field of humanoid robot motion control based on deep reinforcement learning, can solve the problems of slow training, poor anti-interference ability, difficult parameter adjustment, etc., to improve stability and reliability, improve The effect of learning speed and improving training efficiency

Pending Publication Date: 2020-07-03
CENT SOUTH UNIV
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Problems solved by technology

However, compared with wheeled or tracked robots, humanoid robots are inherently unstable and require active control to achieve equilibrium due to their limited support area, high center of mass, and limited actuator capabilities
Therefore, the scope of application scenarios of humanoid robots is mainly limited by the balance of humanoid robots and the ability to deal with disturbances and uncertainties.
[0003] Classical control methods propo

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  • Humanoid robot motion control method and system based on deep reinforcement learning
  • Humanoid robot motion control method and system based on deep reinforcement learning
  • Humanoid robot motion control method and system based on deep reinforcement learning

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[0032] The following further describes the present invention with reference to the accompanying drawings of the specification and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0033] Such as figure 1 As shown, the motion control method of a humanoid robot based on deep reinforcement learning of this embodiment includes: S1. Simulation control: acquiring the current state of the humanoid robot, and calculating and determining the humanoid robot according to the current state using a preset deep reinforcement learning model The target angle of each joint; S2.PD control: Through the PD controller, the target angle is used as the control target, and the actual angle and joint torque of the joint are feedback to determine the control torque of the joint, and control the joint action according to the control torque.

[0034] In this embodiment, a specific humanoid robot model is taken as an example for description, such as fig...

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Abstract

The invention discloses a humanoid robot motion control method and system based on deep reinforcement learning. The method comprises the steps that S1, simulation control is carried out, specifically,the current state of a humanoid robot is obtained, and the target angle of each joint of the humanoid robot are calculated and determined through a preset deep reinforcement learning model accordingto the current state; and S2, PD control is carried out, specifically, through a PD controller, the target angle serves as a control target, the actual angle and the joint torque of the joint serve asfeedback, the control torque of the joint is determined, and the joint is controlled to act according to the control torque. The method has the advantages of good control stability, good reliabilityand the like.

Description

technical field [0001] The invention relates to the technical field of motion control of humanoid robots, in particular to a method and system for motion control of humanoid robots based on deep reinforcement learning. Background technique [0002] Humanoid robots have great application potential and can be deployed in environments where the use of wheeled robots is limited, such as terrain with obstacles, narrow and elevated surfaces (such as stairs). However, compared with wheeled or tracked robots, humanoid robots are inherently unstable and require active control to achieve equilibrium due to their limited support area, high center of mass, and limited actuator capabilities. Therefore, the range of application scenarios for humanoid robots is mainly limited by the ability of humanoid robots to maintain balance and cope with disturbances and uncertainties. [0003] Classical control methods propose a variety of motion algorithms, but these algorithms lack versatility, an...

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1664B25J9/1633Y02P90/02
Inventor 任炬许人文张尧学
Owner CENT SOUTH UNIV
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