Biped robot control method and device, electronic equipment and storage medium

By collecting data from the body sensors of the bipedal robot and combining motion models and reinforcement learning control strategies, the problem of unstable movement of bipedal robots in complex environments was solved, and stable movement and adaptive control in various environments were achieved.

CN121325583APending Publication Date: 2026-01-13DIGITAL HUAXIA (SHENZHEN) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511404197.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-28
Publication Date
2026-01-13

AI Technical Summary

Technical Problem

Existing bipedal robots are unstable in complex environments and are not universally applicable. External sensors are easily affected by the environment, resulting in inaccurate data acquisition and delays.

Method used

By collecting observation data through the joint encoders, inertial measurement units, and foot force sensors of the bipedal robot body, and combining the motion model and unified reinforcement learning control strategy, the Kalman filter algorithm is used to correct the data and optimize the control parameters to achieve adaptive stable motion.

Benefits of technology

This technology enables robots to move stably in various complex environments, avoids the influence of external environments, adapts to different platform hardware conditions, and improves the stability and adaptability of movement.

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Abstract

The invention discloses a biped robot control method and device, electronic equipment and a storage medium. The method comprises the steps that actual observation data at the current moment are collected through a target sensor of a biped robot body; wherein the target sensor comprises a joint encoder, an inertia measuring instrument and a plantar force sensor; predicting a state prediction value at the current moment by combining the state prediction value at the previous moment and a motion model of the biped robot; predicting predicted observation data at the current moment by using the state predicted value at the current moment based on the relationship between the observation data and the state data; correcting the state prediction value at the current moment based on the deviation between the actual observation data at the current moment and the prediction observation data to obtain a state correction value at the current moment; selecting a control parameter in a current state for the unified reinforcement learning control strategy through the correction function based on the state correction value at the current moment, and correcting the control parameter to obtain a current control parameter; and controlling the current biped robot according to the current control parameters.
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