Daxiao Robot Deploys 'Dual-Brain' Architecture in Chinese Hotel Laundry Scenario, Self-Test Shows Task Time Cut to 18.6 Seconds
Daxiao Robot uses a physics-aware world model and cloud-edge decoupling to solve cold-start and latency issues in hotel laundry tasks, reporting a >98% reduction in execution time.
According to QbitAI, Chinese embodied AI company Daxiao Robot recently deployed its "dual-brain collaborative architecture" and Embodied Recursive Self-Improvement (RSI) technology for the first time in a hotel laundry scenario. The solution aims to address the challenges of cold-starting and high latency when robots handle flexible objects and execute long-chain tasks in real, non-standardized environments.
In terms of environment modeling, Daxiao adopted a world model named Mundo. Unlike traditional static digital twins that only perform geometric appearance scanning, Mundo can input photos and replicate an interactive digital hotel containing physical attributes such as object mass and friction. This allows the robot to complete navigation, operation training, and strategy optimization in a digital "driving school" before entering the real site, significantly compressing on-site deployment debugging time.
At the task execution level, the team adopted a strategy of decoupling cloud and edge computing. The cloud-based general brain (GPT-Astra) is responsible for task decomposition, logical reasoning, and generating solidified Skills, while the local execution brain relies on cameras and robotic arms to quickly call these solidified skills. Test data shows that running a full process purely through real-time cloud decision-making takes over 30 minutes, whereas with this division of labor, the local base model completes the same round of tasks in just 18.6 seconds, reducing time consumption by more than 98%.
Additionally, Daxiao introduced the Embodied RSI mechanism to handle unexpected situations in the real world. When changes in lighting or object positions cause edge-side processing failures, the cloud brain re-intervenes to analyze and generate new versions of Skills for distribution. In a 57-hour real-machine experiment targeting the single skill of "retrieving clothes from a dryer," the robot completed 165 consecutive rounds of testing, producing 37 improvement plans. Ultimately, the key metric of residual jeans inside the drum dropped from an average of 48% to 32%, reaching 19% in the best instance.