Kairos 3.0-4B - DaXiao Robotics' open-source embodied native world model

What is Kairos 3.0-4B?

Kairos 3.0-4B is an open-source embodied native world model from DaXiao Robotics, pioneering an integrated architecture of "multimodal understanding-generation-prediction". As the world's first lightweight 4B model capable of end-side robot body control, it achieves 1:1.5 real-time generation on the THOR platform, with inference speed 72 times faster than Cosmos 2.5. The model possesses extreme physical causal consistency, can generate 7-minute long continuous interactive videos, supports cross-body generalization, and allows the same "brain" to drive multi-form robots, providing a core engine for the large-scale deployment of embodied intelligence.

Main functions of Kairos 3.0-4B

• Physical-level world understanding and generationIt accurately reproduces real physical effects such as natural light and shadow, fluid dynamics, and rigid body mechanics. When pouring water, the total amount of liquid is conserved, and when stacking stones, it follows the laws of gravity and support.
• Long-term dynamic interactionIt can generate a 7-minute continuous interactive video, and combine it with an agent to realize a complete home task process such as tidying up the desktop, doing laundry, and making breakfast.
• Robot body controlThe model can directly output omnidirectional control commands from the robot's upper limbs and fingers to its lower limbs, enabling real-time "think and do" responses on the edge platform.
• Cross-ontology generalizationSupports multiple robot forms, including single-arm, dual-arm, and dexterous hand robots, and can be adapted to mainstream hardware such as Zhiyuan, Songling, and Unitree without additional training.
• High-efficiency data simulationAs a low-cost data simulator, it scales up training data, addressing the industry pain point of scarce real-device interaction data.

Technical principles of Kairos 3.0-4B

• Native Embedded ArchitectureUnlike "modified" solutions that simply add motion interfaces to large models, Kairos designs its architecture from the ground up for the robot's real-world operation, using the physical and causal laws of nature as its cognitive foundation, thus achieving a leap from "behavioral imitation" to "physical-level deep understanding".
• Multimodal integrated frameworkIt integrates the three capabilities of "understanding, generation, and prediction", embeds physical laws and causal thinking chains into the model decision-making process, is compatible with visual, text, and sensor instructions, and accurately analyzes physical constraints such as forces, center of gravity, and friction of objects.
• Triple data fusionIt deeply integrates three types of data: real robot interaction, structured human behavior, and thought chain text, breaking down the barriers of diverse data and achieving strong generalization and reliable deployment with better models and data scale.
• Agent intelligent agent technologyThrough hierarchical instruction parsing and structured decomposition, it refines the prediction of spatiotemporal evolution and interaction logic, and relies on a self-reflection mechanism to achieve closed-loop iterative optimization, thus completing the information of the continuous world.
• High-efficiency inference operators: A self-developed hybrid temporal linear attention operator achieves inference speed 72 times faster than mainstream models with a lightweight 4B parameter, generating data in real time on the edge while maintaining extremely low GPU memory usage.

Kairos 3.0-4B project address

• GitHub repository: https://github.com/kairos-agi/kairos-sensenova
• HuggingFace model libraryhttps://huggingface.co/kairos-agi/kairos-sensenova-common

Application scenarios of Kairos 3.0-4B

• Industrial manufacturingIt is used for simulation training and trajectory planning of long-process assembly tasks, and can simulate complex operations in a virtual environment to reduce the cost and risk of debugging on real machines.
• Home servicesIt drives robots to complete daily household chores such as organizing, laundry, cooking, and item delivery, and its 7-minute long-term interaction capability supports continuous service scenarios.
• Logistics warehousingIt enables dynamic planning and real-time control of tasks such as cargo handling, sorting and palletizing, and shelf inspection, and is compatible with different types of robotic arms and mobile robots.
• Business ServicesThe model can support scenarios such as tour guide reception, food delivery, cleaning and maintenance, and its cross-entity generalization capability allows the same system to quickly adapt to robot hardware in different stores.
• Data Acquisition and SynthesisAs a high-efficiency data simulator, it generates physically consistent training data at low cost and scale, alleviating the bottleneck of scarce real-device interactive data.