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Smart system helps humanoid robots coordinate their whole bodies for chores

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Smart system helps humanoid robots coordinate their whole bodies for chores

Shanghai-based Archon Robotics has released its first demonstration of Whole-Body Intelligence (WBI), which coordinates humanoid robots’ movements with their understanding of tasks.

The system links high-level task planning with coordinated full-body motion, enabling robots to perform complex activities in real-world environments.

The demonstration features popular humanoid models performing tasks such as picking up objects, navigating steps, clearing tables, operating dishwashers, and organizing refrigerators. 

The firm said its approach uses human data for pretraining and real-robot data for post-training, with the showcased full demonstrations running autonomously at normal speed.

Archon’s whole-body intelligence framework is designed to help humanoid robots translate task instructions into coordinated physical movements. The Shanghai-based company aims to develop general-purpose AI models that combine task understanding, motion planning, balance, and precise execution across different humanoid platforms.

The approach, called Whole-Body Intelligence (WBI), combines large-scale human whole-body data for pretraining with real-robot demonstrations for post-training. The debut demonstration shows humanoids picking up objects, navigating steps, clearing tables, operating dishwashers, and organizing refrigerators.

Archon’s framework aims to coordinate a robot’s entire body rather than treat individual actions as isolated commands. Its demonstration highlights two components, S1 and S0, which handle different aspects of the process. S1 processes task-related information and supports behavioral decision-making, while S0 focuses on whole-body motion control, including coordination, stabilization, and precise execution. Archon describes S0 as functioning like a cerebellum, translating targets into executable movements while maintaining physical stability.

The company’s July technical blog described a broader four-layer architecture that also includes task planning and motion generation.


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