Alibaba has released RynnBrain, a foundational AI model for robotics, which set new records in 16 embodied intelligence evaluations, outperforming mainstream models from Google, NVIDIA, and others. By integrating spatiotemporal memory and reasoning capabilities, RynnBrain enables robots to perform autonomous task planning and execute multiple tasks consecutively in complex scenarios.
On February 10, $Alibaba (BABA.US)$ Officially launched the RynnBrain, an AI foundational model for robotics. This open-source model aims to equip robots with perception, decision-making, and execution capabilities, promoting their autonomous task completion in real-world scenarios.
RynnBrain, independently developed by Alibaba's DAMO Academy, features core capabilities such as environmental interaction, spatiotemporal understanding, and task decomposition planning. The model can assist robots in completing object recognition and localization, motion trajectory prediction, and achieving precise navigation and autonomous operation in dynamic and complex environments like kitchens and factory assembly lines.
According to test data released by Alibaba, RynnBrain has demonstrated outstanding performance in multiple authoritative evaluations, surpassing $Alphabet-C (GOOG.US)$ Gemini Robotics-ER 1.5 and $NVIDIA (NVDA.US)$ Cosmos-Reason2, among other mainstream industry models. This model has set new records (SOTA) across 16 embodied open-source evaluation leaderboards.
Currently, robotics technology is becoming a key arena in global technological competition and industrial transformation, with cutting-edge directions like humanoid robots being viewed as a crucial driver reshaping the ecosystems of manufacturing and service industries. Alibaba’s release of this foundational model, which serves as a "thinking brain," not only demonstrates its continued investment in core AI technologies but also highlights a clear path toward promoting technical standardization and industrial implementation.
Breakthroughs in Spatiotemporal Memory and Reasoning Capabilities
The core technological breakthrough of RynnBrain lies in its integration of spatiotemporal memory and spatial reasoning capabilities into robotic systems for the first time. By embedding these two critical capabilities into the model architecture, robots can maintain continuity and consistency in their workflow while executing multitasking operations.
In practical applications, robots equipped with this model can accurately remember the spatiotemporal nodes and execution progress of Task A if interrupted and redirected to Task B. After completing Task B, they autonomously resume the previously interrupted workflow of Task A.
This model integrates multidimensional capabilities such as environmental cognition, precise positioning, logical reasoning, and task planning, and exhibits strong scalability. Based on the RynnBrain framework, developers can efficiently train specialized models for navigation, planning, and motion control in various scenarios with just hundreds of fine-tuning data points.
Comprehensive Open-Source Strategy
DAMO Academy has open-sourced all seven models in the RynnBrain series, covering specifications ranging from the 2-billion-parameter version to the 30B Mixture-of-Experts (MoE) architecture. The series, trained based on the Qwen3-VL vision-language model, is now available for access on platforms such as Hugging Face and GitHub.
Among these, the industry's first embodied model with 30 billion parameters using the MoE architecture aims to enhance the fluidity and responsiveness of robotic movements. To standardize evaluation criteria, DAMO Academy has simultaneously released a new benchmark, RynnBrain-Bench, which focuses on fine-grained spatiotemporal task assessments, filling an existing gap in industry evaluations.
Zhao Deli, the head of the Embodied Intelligence Laboratory at Damo Academy, stated:
"RynnBrain marks the first time that deep understanding and reliable planning for the physical world have been achieved by a brain-like system, taking a crucial step toward general embodied intelligence under a hierarchical cerebrum-cerebellum architecture. We look forward to it accelerating AI’s transition from the digital world to real-world physical scenarios."
Acceleration of Industrialization in Embodied Intelligence Layout
Chinese technology companies are continuously increasing their open-source investments in the field of artificial intelligence, fostering a technical development path characterized by open collaboration. In cutting-edge areas like embodied intelligence, open-source strategies help pool global developer resources, accelerating technological iteration and ecosystem construction.
Robotics technology is considered a key area driving industrial upgrading. At the policy level, intelligent robots, including humanoid robots, have been explicitly identified as a key development direction, aiming to reshape operational models in manufacturing and service industries through technological innovation.
DAMO Academy continues to promote technological openness in this field, having successively open-sourced several embodied intelligence models such as WorldVLA, which integrates world models and visual-language models, and RynnEC, an environmental understanding model. Additionally, it launched the industry's first robot context protocol, RynnRCP, dedicated to building deployable, scalable, and continuously evolving embodied intelligence systems.
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Editor/Doris
