Tencent Hy-Embodied Foundation Models

Tencent's embodied-AI foundation model series built on Hunyuan, covering visual understanding (VLM-1.0), world cognition (RxBrain-1.0) and a vision-language-action model (VLA-0.5), unveiled and open-sourced at WAIC 2026

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In-depth Report

In July 2026, Tencent officially released a complete set of embodied intelligent base model systems - the Hy-Embodied series - at the World Artificial Intelligence Conference (WAIC) in Shanghai, including the visual understanding model VLM-1.0, the world cognition model RxBrain-1.0, and the joint vision-language-action model VLA-0.5. This is the first time Tencent has systematically integrated "perception-cognition-action" into a complete embodied intelligence closed loop. All three models are built based on Tencent's Hunyuan large model and were open sourced simultaneously on the day of release. At the same time, Tencent also launched the continuous online embodied intelligence Apexio, the embodied native framework TairosAgent, and a comprehensive upgrade of the Tairos (titanium screw) open platform, covering the entire link from cloud computing power to the robot body. The core judgment of this solution is that the current most intelligent AI is still essentially a "brain in a vat" - it is good at reasoning and dialogue, but lacks a closed loop for direct interaction with the physical world. The Hy-Embodied series targets this gap.
The Hy-Embodied series is jointly developed by Tencent Robotics X Laboratory, Futian Laboratory and Tencent Hunyuan. Tencent Robotics The positioning division of labor between the three models is very clear: VLM-1.0 is like the "right brain", responsible for understanding images, spaces and scenes. It is close to the same level of visual understanding performance while consuming only about one-tenth of the computing power of the previous generation flagship; RxBrain-1.0 Like an embodied "brain", it simultaneously completes text reasoning and visual target imagination in a unified architecture - it not only tells the action model "what to do next", but also shows "what the world should look like after doing it" in the form of images; VLA-0.5 is like a bridge between the "cerebellum" and the body, converting high-level cognitive goals into continuous, error-correctable physical action sequences. This release is not simply a display of academic achievements. Tencent simultaneously announced at the WAIC forum that VLA-0.5 has entered a daily chemical factory for actual production testing. On a real production line with high mixing, small batches, and frequent SKU iterations, the operation success rate exceeds 95%, the single-piece cycle is faster than 6 seconds, and the time for adding a new SKU is less than 3 days for data collection and model post-training.
The core capability of VLM-1.0 is open-world visual understanding. It receives multi-view image input and outputs spatial semantic representation - object position, operability judgment, scene layout, etc. Compared with the previous generation VLM, it maintains close performance on spatial understanding benchmarks such as CV-Bench and EmbSpatial while reducing the amount of calculation by about 90%, which means that it is more suitable for deployment on robots with limited computing power. RxBrain-1.0 is the most technically highlighted part of the entire model. It adopts the Mixture-of-Transformers architecture and uses modality as the route to route text Token, input visual Token and generated visual Token to dedicated computing paths respectively. Text and visual understanding each account for about 1.5B parameters, and visual generation adds an additional 2.4B of FFN Expert, with a total parameter count of about 6.2B. It scored 82.4 points in the GenEval Vincentian graph evaluation, which is the same as the 82 points of the generation-specific model BAGEL, indicating that the unified architecture does not sacrifice generation quality. In terms of embodied reasoning, its comprehensive planning score on RxBrain-Bench is 0.68, far exceeding the modular solution - in comparison, the Agent composed of Qwen3-VL-2B and Qwen-Image-Edit scored only 0.431. In joint tasks such as multi-step planning and visual target imagination, the advantages of the unified model are very obvious: modular systems are prone to problems such as language duplication, target images being disconnected from the plan, and state drift during multi-step execution. However, RxBrain alternately generates subtask text and target images in the same context, and re-injects the previous round of generation results into subsequent planning. The consistency is far better than external orchestration. RxBrain also further extends the action generation branch Action MoT. It configures independent attention projection and FFN for action tokens while retaining global attention interactions with other modalities. Action parameters are initialized by the visual understanding branch, and a gated fusion mechanism is introduced - action experts can selectively borrow world state prediction and planning features from visual generation experts by channel. In the real machine verification, on the DOBOT X-Trainer and Ark Infinite A5 robotic arms, the average success rate of the three multi-stage tasks of placing tableware, folding and storing glasses, and throwing away garbage reached 87%, which is significantly improved compared to π0's 68% and π0.5's 82%.VLA-0.5 focuses on fact verification. It ranked first in the overall simulation ranking in the five dimensions of the third-party RoboDojo evaluation - generalization, memory, fine operation, long-range execution, and open semantic understanding. It also ranked first in the two dimensions of long-range tasks and memory tasks. More than 10,000 hours of high-precision human teaching data are its training basis. At the agent level, Apexio’s architecture is quite ingenious. It consists of three layers of capabilities that are online at the same time at different frequencies: the top-layer cognitive system wakes up on demand to do deep thinking, the middle-layer perception and action system continuously receives multi-modal information at about 15Hz and adjusts quickly, and the bottom-layer execution system runs at a higher frequency and instantly handles collisions and imbalances like conditioned reflexes. The system is simultaneously driven by two main lines: "goal drive" (completion of tasks) and "survival drive" (maintaining safety, controlling energy consumption). Even without external instructions, it continues to sense and prepare for the next action. TairosAgent is a framework built natively for the robot body from the first day of design, rather than being modified from a general framework. It can connect to different
All Hy-Embodied series models are open source. Model weights can be downloaded directly on GitHub and HuggingFace. VLM-1.0 and VLA-0.5 use the MIT license, and RxBrain-1.0 also adopts an open license. Tencent’s monetization path is more focused on the platform layer and cloud service layer. The Tairos platform is open to robot and system developers, providing open source models, agents, development tools and one-stop services. Tencent Cloud has built a three-layer infrastructure system from computing power base, model service to perception interaction, and launched cloud-based EaaS (Embodied-AI-as-a-Service). This means that customers can call on embodied intelligence capabilities on demand without having to build a complete infrastructure themselves. In addition, Tencent Cloud HAI (high-performance AI computing power), multi-network aggregation acceleration, TRRO and other services are also linked with the Hy-Embodied series. Industrial customers can directly obtain the computing power, storage and network capabilities required for training and deployment through Tencent Cloud.
positive review Feedback from the third-party community focused on a few points: The engineering trade-offs of the unified architecture were considered very pragmatic. Many AI practitioners mentioned in the HuggingFace comment area that RxBrain's approach of cramming textual reasoning and visual imagination into about 6B parameters is "more worthy of learning than simply heaping parameters." The real machine verification data is public and corresponds to the open source model one-to-one, and has also received praise. In Reddit's r/MachineLearning section, a user commented, "Finally a major company is not just publishing papers when it comes to embodied intelligence." The feedback from the Chinese community is more focused on the recognition of the "brain in a vat" discussion. Highly praised answers on Zhihu generally believe that Zhang Zhengyou's judgment on WAIC "pointed out the fundamental limitations of the current large model" and that the release of Hy-Embodied "at least shows that Tencent is taking a different path." negative feedback Criticisms focused on the distance of actual availability. RxBrain's score in the 8-step long-range planning scenario dropped from 0.69 to 0.55. The trend of getting worse step by step shows that the accumulation of errors in visual imagination is still a bottleneck. Some technical comments pointed out that although RxBrain has demonstrated the potential of a "unified brain", "now is not the time" for it to run on a factory production line for hours without errors. There's also some dissatisfaction with pricing. Although the model is open source, the cost of Tencent Cloud's computing power is not low when it comes to running it in a factory. A Zhihu user calculated an account and deployed a complete Hy-Embodied solution. The monthly GPU rental cost alone is tens of thousands of yuan. The Tairos platform’s enterprise-grade services aren’t free, either. Usage scenarios In addition to industrial production lines, Tencent also disclosed two implementation scenarios of shopping guides and elderly care services. In the shopping mall navigation robot scenario, VLA-0.5 can complete tasks such as leading the way, introducing products, and answering inquiries based on natural language instructions; in the elderly care scenario, the model is mainly responsible for medicine delivery, fall detection response, and simple companion dialogue.
Judging from industry reaction, the release of the Hy-Embodied series is regarded as an important signal: major Chinese manufacturers are shifting from "model layer competition" to "application layer competition." Different from the cautious stance of overseas companies such as Google and Meta in the field of robotics, Tencent directly provided a full-stack solution from basic models to cloud services to open platforms this time, and made it clear that it is open source and commercially available. Ranking first in RoboDojo's comprehensive ranking means that VLA-0.5's comprehensive capabilities, at least in the simulation environment, have surpassed the current mainstream open solutions. However, it should also be noted that there is still a gap between simulation and real machine: a model that performs well in RoboDojo may perform poorly in the physical world due to issues such as sensor noise, latency, and hardware differences. Tencent only tested three types of tasks (arranging tableware, storing glasses, and throwing away trash) on real phones, and the coverage was limited. From the perspective of the competitive product landscape, the domestic embodied intelligence track is already quite crowded. ByteDance has the GR-2 series, Huawei has the Pangu body model, and Xiaomi has CyberOne and the force control algorithm team behind it. Tencent's advantages lie in the open source of all models, the synergy of the Hunyuan large model family bucket, and the ecological accumulation of the Tairos platform. The disadvantage is that compared to Byte and Xiaomi, Tencent's layout in hardware-especially humanoid robots-is relatively weak, and it currently relies more on partners.
One point of controversy worth noting is the actual meaning of “open source.” Although the weights and inference codes of the three Hy-Embodied models are open source, the training data (more than 50,000 hours of embodied data) are not open. The scale and quality of training data directly determine the upper limit of the model's generalization ability. When other teams cannot reproduce or customize it, the actual reusability of the so-called "open source" will be compromised. Another risk comes from the maturity of industry applications. The actual measured data of the daily chemical factory looks beautiful (>95% success rate), but this is just a scenario where SKU changes frequently but the operation is relatively simple. There is no public verification data on whether the current capabilities of embodied intelligence models are sufficient to support industries such as automotive assembly and precision electronics manufacturing that have higher requirements for accuracy and fault tolerance. In addition, although Apexio's three-layer architecture is technically convincing, from three-layer "simultaneous online" to truly stable implementation involves a lot of system engineering and hardware adaptation work. As a Zhihu respondent with a background in robotics engineering commented: "The demo shown in the laboratory and running two shifts on the production line without any problems are two completely different things."
The Hy-Embodied series is most suitable for the following two types of users: The first type is robot system and complete machine developers, who can directly download open source models for secondary development and adaptation, and use the tool chain of the Tairos platform to reduce prototype development costs. The second category is industrial customers, who can quickly test the feasibility of embodied intelligence in production lines through Tencent Cloud's EaaS service without having to build their own training and deployment infrastructure. Scenarios that are less suitable include: precision assembly lines that require extremely high positioning accuracy, safety-critical applications (such as medical surgical assistance), and scenarios that need to run on extremely low-power devices at the edge. In these cases, skill-oriented dedicated models or rule systems may still be a safer option for now.
Tencent Hy-Embodied series is one of the most important open source solutions in the field of embodied intelligence in 2026. Its core contribution in model architecture—unifying textual reasoning and visual imagination into a continuous cognitive sequence—does respond to the “brain in a vat” problem. But to be objective, from "letting robots understand the world" to "making robots work stably in the real world", the Hy-Embodied series has only completed the first step. The entire solution still has a long way to go in terms of stability for long-distance missions, availability of training data, and adaptability to complex industrial scenarios.

User Reviews

STurner_2020

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腾讯这波发得挺猛的，VLM、RxBrain、VLA 三件套一口气全开源了，关键是真有工厂落地数据，不是只发论文。

MDiaz1689

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RxBrain 那个统一文本推理和视觉想象的思路有点意思，比单纯的 VLM+World Model 拼起来更优雅。

Emma_Hernandez

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看了张正友关于「缸中之脑」的访谈，说得挺透的。现在大模型确实像关在缸里的大脑，知识很丰富但没有身体去验证。

BrandonPowellK87

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日化品工厂那个落地案例有点说服力，95% 成功率、6 秒/件的节拍、新增 SKU 只要 3 天适配，这数据在 WAIC 上算很能打的了。

lazysnake753

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Hy-Embodied-VLM-1.0 用 A3B 的规模做到接近上代 A32B 的效果，这个效率提升是真的香，机器人端部署不用背那么重的计算包袱了。

AnnaPetersonIII99

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腾讯坚持不做机器人硬件本体，这点倒是挺清醒的。做平台和大脑的逻辑对，但问题在于——机器人厂商愿不愿意把「大脑」外包给腾讯？

TheElisaHidalgo_2024

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全栈都开源了，Apache-2.0 协议，这点比字节和小米厚道不少。不过训练数据没开源，5 万小时的数据不放出来，别人想复现定制还是难。

Jonathan77z

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Apexio 那个三层架构——认知、感知行动、执行——频率从低到高分三层，挺像人脑的分层设计。说响应时间从几十秒缩短到 2-3 秒，这个提升确实大。

PEbel

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RoboDojo 仿真综合第一确实好看，但仿真和真机之间的差距懂的都懂。腾讯自己也不敢多吹仿真，重点在讲工厂实测。

MrLillySveum

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说到底 RxBrain 目前最大的瓶颈还是视觉想象的误差累积问题，八步规划从 0.69 掉到 0.55，真的跑长程任务还是悬。

Bryan_Williams_2021

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智元、星海图它们都自研大模型了，腾讯的「平台+生态」逻辑在移动互联网时代没错，但在具身智能这个领域，数据是核心生产资料，机器人厂商真的甘心把自己大脑交给别人？

CClark_X266

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VLA-0.5 那个 UMI 采集系统挺扎实的，亚毫米级精度、一万小时人类示教数据，这是真金白银砸出来的。不是那种「在仿真里跑了几个 benchmark 就吹上天」的路线。

beautifulcat979

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腾讯这次把 WorkBuddy APP、ADP 4.0 海外版和 Hy-Embodied 一起发了，覆盖面确实广。但具身智能这块还是觉得偏早期，离真正的通用机器人还有很大距离。

BeverlyRobinsonSr

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RxBrain 的 GenEval 文生图得分 82.4，和 BAGEL 的 82 分持平，一个具身认知模型能做出这个生成质量确实意外。说明统一架构没有牺牲生成能力。

SAmen

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WAIC 转了一圈，腾讯的展台是人最多的之一。现场那个按摩机器人「小六」的力控确实惊艳，听说基于腱绳传动，能复现人手的按摩轨迹。

MMitchellJr86

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对比前年 WAIC 的 PPT 发布，腾讯这次有了 Tairos 平台的一年沉淀、有了工厂落地数据、有了开源生态，进步还是看得见的。但具身智能这个赛道还有很长的路要走。

Heather_Ramos_2024

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世界模型的概念今年被炒得太火了，各家都在说自己做了世界模型。张正友说「世界模型还没有统一定义」，还是比较诚实的。

rtko4f2uts

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腾讯的 EaaS 思路对中小机器人厂商很友好，不用自建训练和部署基础设施。但 GPU 的租赁成本算下来一个月也得几万块，也不是小数目。

GloriaMiller_2021

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Hy-Embodied 这个命名体系挺好的，VLM 是眼睛、RxBrain 是大脑、VLA 是小脑。对开发者来说很清楚该用哪个。

Sharon_Cooper_99_684

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腾讯如果不做硬件本体，那和宇树、智元它们的竞合关系会很微妙——既是平台提供方，又是投资人。这种双重身份怎么处理还没看到答案。