Robot control method and system based on dual system, training method and system

By having the decision-making and execution systems work together in a dual-system architecture, the decision-making system breaks down complex tasks and outputs control conditions, while the execution system adaptively generates multimodal thought chains. This solves the problems of insufficient logical reasoning and lack of fine granularity in long-term robot tasks, thereby improving task completion and success rate.

CN122401441APending Publication Date: 2026-07-17XINGHAITU (SUZHOU) ARTIFICIAL INTELLIGENCE TECHNOLOGY CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XINGHAITU (SUZHOU) ARTIFICIAL INTELLIGENCE TECHNOLOGY CO LTD
Filing Date
2026-06-12
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing robot control systems suffer from insufficient high-level logical reasoning and long-term task decomposition capabilities in long-term tasks, and lack fine-grained directionality, resulting in poor task completion and catastrophic forgetting problems.

Method used

A robot control method based on a dual system is adopted. Through the collaborative work of the decision system and the execution system, the decision system performs cognitive decomposition of complex tasks and outputs control conditions, while the execution system adaptively generates a multimodal thought chain based on the control conditions, thereby realizing hierarchical collaboration between cognition and execution.

Benefits of technology

It improves the completion rate and success rate of long-term tasks, solves the problem of the lack of fine-grained directionality in the execution system of long-term tasks, and significantly improves the success rate of multi-stage operations.

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Abstract

本发明公开一种基于双系统的机器人控制方法及系统、训练方法及系统,涉及机器人领域,包括:获取长程指令,并采集当前环境图像;将环境图像和长程指令输入决策系统,由决策系统生成控制条件和子任务指令;将环境图像、控制条件和子任务指令输入执行系统,执行系统根据控制条件确定思维链的生成范式,并基于生成范式自回归地生成与控制条件对应的多模态思维链;使执行系统根据多模态思维链输出机器人动作;当前动作执行完成后,循环调用决策系统进行下一步规划,直至长程任务结束。本发明通过决策系统与执行系统的分层协同,解决了长程任务完成度差的问题。
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