EEmbodied AI Hub
IndustryDeploymentEditor’s pick

Chaowei Power and Peking University Healthcare: A Pragmatic Path for Embodied AI in Hospitals

Chaowei Power and Peking University Healthcare partner to build a step-by-step embodied AI deployment path for hospitals, focusing on simulation, validation, and application. The collaboration uses Chaowei's world model engine and data collection headset to create high-fidelity training environments, addressing the lack of real medical training data. The approach prioritizes safety over speed, targeting tasks like tube sorting and drug delivery.

Peking University Healthcaremedical simulationChaowei Powerworld modelfeed:qbitaideploymentKAI Haloqbitai

Source: 量子位 · July 29, 2026

Share this article so more people can see it

Shenzhen-based Chaowei Power Intelligent Technology (超维动力) has announced a deep collaboration with Peking University Healthcare Management (北大医疗) to deploy embodied AI in medical settings. The partnership focuses on data collection, world model development, and simulation training, aiming to create a replicable path from "simulation training" to "scenario validation" to "hospital application."

For embodied AI to truly enter hospitals, it must first understand the environment and medical workflows. The biggest bottleneck is the lack of realistic training environments and expert guidance. Chaowei Power brings a full-stack embodied AI large model platform, including the KAI World Model engine, KAI Halo first-person data collection headset, and a medical digital asset library. Peking University Healthcare provides real clinical scenarios and professional medical expertise.

The first step is simulation: Chaowei uses its KAI World Model to recreate hospital rooms and operating theaters in virtual space, allowing robots to iterate safely. The KAI Halo headset records real medical procedures, which are then processed into high-quality multimodal training data, effectively creating digital mentors.

Next is training: Chaowei builds a medical digital asset library with real drug and instrument images, enabling interactive simulation. Developers can combine assets to create diverse training scenarios without physical consumables. Robots undergo large-scale operational learning, accumulating experience through repeated practice.

The final step is validation and application: All capabilities must pass rigorous testing in simulated hospital environments before real-world deployment. Priority tasks include tube sorting, drug delivery, and bed transfer—critical yet low-risk starting points for embodied AI in healthcare.

This pragmatic approach emphasizes safety over speed. Chaowei and Peking University Healthcare are committed to a steady, verifiable path, ensuring every capability is proven before entering clinical settings. The partnership aims to provide a reliable technology engine for smart hospitals and high-quality medical services.

Source: 量子位.

Discussion

Tell us what you think — comments make stories more useful for builders and founders.

Tell us what you think!

Chaowei Power and Peking University Healthcare: A Pragmatic Path for Embodied AI in Hospitals

Have an account? Log in to use your display name and avatar.

Email is optional and never shown on the page.

More insights

IndustryDeploymentFeatured雷峰网

Video Generation Is Not Planning: Georgia Tech's Danfei Xu Proposes Factor Graphs to Bridge the Action Gap

Danfei Xu argues that high-fidelity video generation does not equal physical planning. He proposes compositional world models using factor graphs and temporal attention to enable test-time skill composition, improving success on out-of-distribution tasks. The approach reveals a video-action gap where action models lag behind visual generalization.

Read
IndustryDeployment雷峰网

Xingchi Power and PKU Healthcare: A Pragmatic Path for Embodied AI in Hospitals

Xingchi Power and PKU Healthcare are collaborating on a three-step pipeline—simulation training, scenario validation, and hospital deployment—to bring embodied AI into medical settings safely. The partnership leverages Xingchi's world model engine and data headset to create high-fidelity virtual hospitals, addressing the lack of training environments and expert data. This pragmatic approach prioritizes safety over speed, focusing on tasks like medication sorting and delivery.

Read
IndustryDeployment雷峰网

RSS 2026 Debate: Data Source, Control Modality Split Robotics Community

At RSS 2026, five experts debated whether world models and policy models should be unified or separate, whether control should rely on pixels or actions, and whether training data should come from the internet or embodied robots. The 'separate' camp won the first round, 'pixel' vs 'action' ended in a draw, and 'internet data' won the third round 61% to 39%. Key takeaways: modular design is preferred for debugging, video alone is insufficient for contact-rich tasks, and hybrid data strategies are

Read