Double-arm smart teleoperation method and system based on motion redirection and adaptive force

By combining SAG-GCN and VLM/MPC, high-precision motion redirection and adaptive force control for cross-platform robot teleoperation are achieved, solving the problems of insufficient motion mapping accuracy and gripping force adjustment in cross-platform teleoperation, and achieving natural, stable and safe operation results.

CN121670679AActive Publication Date: 2026-03-17SUN YAT SEN UNIVERSITY SHENZHEN +1
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Patent Information

Application Number
CN202610173452.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-06
Publication Date
2026-03-17
Estimated Expiration
2046-02-06

AI Technical Summary

Technical Problem

Existing robot teleoperation technology suffers from insufficient motion mapping accuracy and limited generalization ability in cross-platform applications. Furthermore, existing grasping control is unable to dynamically adjust the grasping force according to object properties and the environment, resulting in unnatural, unstable, or damaging operations.

Method used

We employ a spatial attention-gated graph convolutional network (SAG-GCN) for human-computer topology modeling and cross-platform action redirection, and combine it with a visual language model (VLM) and model predictive control (MPC) to achieve adaptive force control based on object semantics, thereby optimizing the grasping force through visual perception and force feedback.

Benefits of technology

It achieves high-precision motion reproduction and safe and compliant grasping in complex dynamic environments, improving the naturalness, stability and security of cross-platform teleoperation.

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Abstract

The invention discloses a two-arm smart teleoperation method and system based on motion redirection and self-adaption, and relates to the technical field of robots, and the method comprises the steps: determining the posture of a human hand and the posture of a human arm according to a human body image; the robot is redirected according to the human hand posture and the human arm posture through the space attention gating map convolutional network, and the joint angle of a mechanical hand of the robot and the joint angle of a mechanical arm of the robot are correspondingly obtained; the mechanical arm is driven according to the joint angle of the mechanical arm so that the mechanical arm can grab the target object; and target force needed for grabbing the target object is determined, and the mechanical arm is driven to grab the target object according to the target force and the joint angle of the mechanical arm. According to the method, topology modeling and cross-platform action redirection between the human body and the robot are achieved through the space attention gating map convolutional network, pairing data dependence is eliminated, and therefore the high precision of action reproduction and the safety and flexibility of the grabbing process are both considered in the complex dynamic environment.
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