Long-horizon tasks are emerging as a critical focus within the embodied intelligence industry.

In the recent past, robot demonstrations have largely centered on isolated actions like grasping or folding clothes. However, for real-world deployment in homes and factories, a single assignment often requires the seamless execution of a dozen or even dozens of sequential steps.

The longer the task, the greater the demands placed on the robot. Beyond executing individual movements, the machine must continuously retain its objective, decompose the mission into subtasks, plan its path, and promptly correct course after environmental changes or operational setbacks.

A failure at any single point can jeopardize the completion of the entire operation. This complexity is making long-horizon tasks a key benchmark for assessing the comprehensive capabilities of embodied intelligence.

Beginning with the World Artificial Intelligence Conference in July 2026, several companies, including Qianxun Intelligent, used the event to spotlight their robots' proficiency in handling long-horizon tasks.

After showcasing its robot Moz1 performing a "living room tidy-up" at the World Artificial Intelligence Conference on July 17, the company has now revisited this challenging long-horizon assignment at the 2026 World Robot Conference, which opened on August 19.

In detail, upon receiving the "tidy the living room" command, the robot identifies items such as cans, bowls, trash, and toys. It then autonomously decomposes the task, plans its route, and proceeds to execute actions like placing items in the refrigerator, dishwasher, and trash bin.

According to Qianxun Intelligent, over the past 30 days, the overall efficiency of Moz1's "living room tidy-up" has improved by more than 15%. The success rate for certain subtasks has climbed to over 99%, and the time required for navigation goal confirmation has been reduced by nearly 50%.

The gains in long-horizon task efficiency hinge on the smooth integration of multiple components, including the model, agent, navigation, and the robot's physical execution. The model interprets the task, the agent preserves the long-term objective and breaks it down into steps, the navigation system guides the robot to the correct location, and the body performs the specific actions. For instance, if the refrigerator door is left ajar or an item has been moved, the system must continually adapt.

Any delay or failure in one of these links can affect the overall completion efficiency.

Looking ahead, Qianxun Intelligent plans to follow a three-step strategy—"industry first, then commercial services, and finally home use"—to drive the deployment of its general-purpose embodied brain in more real-world scenarios, delivering replicable value while building a foundation of capabilities for general-purpose robots to enter increasingly complex and open everyday living environments.