The announcement that Tencent is open sourcing a full stack of embodied AI models for robotics matters because it turns what used to be bespoke lab technology into infrastructure that others can build on, at a moment when robots are finally starting to benefit from the same foundation model wave that transformed language and vision systems. Instead of releasing one more model and some demos, Tencent is putting a layered robot brain into the open, aimed at perception, world cognition and motor control in real environments, and making it efficient enough to run close to where the actuators live. In parallel, Tencent is extending this approach into a full-stack embodied intelligence solution that spans cloud infrastructure, models, platforms and applications to make real-world deployments more efficient. Additionally, the integration of these models may introduce significant security risks due to expanded attack surfaces in connected business services.
From disembodied chatbots to embodied intelligence
For most of the past decade, progress in AI has been dominated by systems that live entirely in the digital world. Large language models learned to read and write, vision models learned to recognize objects and scenes, and multimodal systems began to combine both, but they rarely controlled physical bodies. Robotics, meanwhile, leaned on a mix of classic control theory, carefully engineered perception pipelines, and narrow machine learning models tuned for specific tasks. The gap between those disembodied models and messy physical reality has been one of the field’s hardest problems.
AI’s biggest challenge is escaping the screen and surviving messy, unpredictable physical reality
Embodied foundation models emerged to close that gap by training large models directly on data from agents interacting with the physical world, including first person robot sensor streams and human teleoperation traces. Tencent’s earlier Hy Embodied 0.5 suite built on this idea with a Mixture of Transformers architecture that separates vision and language computation, offering a compact two billion parameter edge model and a much larger thirty two billion variant for complex reasoning. Trained on more than one hundred million embodied data samples and around two hundred billion tokens, those models developed strong spatial understanding and action oriented reasoning, with the smaller variant outperforming similarly sized models on sixteen benchmarks and the larger one reaching performance comparable to Gemini three point zero Pro. That work laid the groundwork for the more ambitious Tairos release.
The Tairos full stack robot brain
At WAIC twenty twenty six, Tencent’s Robotics X Lab, Futian Lab and Hunyuan team introduced a three layer architecture on the Tairos embodied intelligence platform and made it available as open models: Hy Embodied VLM one point zero for perception, Hy Embodied RxBrain one point zero for cognition, and Hy Embodied VLA zero point five for action. The stack is explicitly framed as a see imagine act pipeline, in which robot sensors feed the perception model, the world model reasons about goals and futures, and the action model translates those plans into continuous motor commands.
Crucially, these models are positioned as shared infrastructure rather than as a proprietary capability. Tencent describes Tairos as a full stack solution that packages models, tools, data services and deployment options in a way that is meant to reduce integration friction for both research labs and industrial robotics teams. That shift echoes what happened in software with operating systems and cloud platforms. In effect, Tairos is intended to be a common foundation for embodied agents, not just a flagship demonstration inside Tencent.
Hy Embodied VLM one point zero the perception layer
Hy Embodied VLM one point zero sits at the perception layer and is built as a next generation embodied vision language base model that focuses on understanding physical scenes from the perspective of an agent. It uses a mixture of experts language backbone known as Hy three A three B together with a Hy ViT two vision encoder, wrapped in an architecture designed for conditional generation over vision and language inputs. The model has roughly thirty billion total parameters, but only about three billion parameters are activated per token thanks to its mixture of experts design, which selects a small subset of specialists for each input.
To evaluate the model’s embodied capabilities, Tencent reports an assessment framework covering thirty seven tasks that test physical state understanding, action change reasoning, and temporal and adaptive reasoning. On those three dimensions, Hy Embodied VLM one point zero achieves scores of sixty eight point six, sixty four point one and fifty seven point four respectively, with an overall average of sixty five point six, which is close to the company’s previous A thirty two B flagship model despite running at an A three B scale and significantly lower computational cost.
In comparative benchmarks, it outperforms peer models of similar size such as Qwen three point six A three B, Cosmos three eight B, and Embodied R one models across both general and embodied evaluation suites. That combination of efficiency and performance is what makes on device deployment on robots plausible rather than aspirational.
Beyond raw scores, the perception model is designed to reason about affordances, spatial layouts and evolving environmental conditions, including understanding which parts of an object can be manipulated, how obstacles and furniture constrain motion, and how the robot’s own viewpoint changes as it moves. It can represent objects, attributes, depth and spatial relationships, interpret robot centric camera perspectives, and link those to physical constraints relevant to a current task, such as reachability and support surfaces.
For long horizon behaviors, it supports multi step planning, visual language navigation, spatial memory, failure diagnosis and counterfactual analysis, giving it a more comprehensive grasp of scenes than earlier dense multimodal models.
RxBrain one point zero the world model and cognition layer
Hy Embodied RxBrain one point zero occupies the cognition layer as a world model that aims to unify understanding, reasoning, imagination and prediction of action consequences in a single embodied system. The version integrated into Tairos uses sixty two billion parameters, reflecting its role as a heavy weight planner rather than a lightweight edge component.
In Tencent’s framing, RxBrain constructs dense multimodal conditions that encode imagined future states, turning high level goals, task plans and counterfactual reasoning into guidance signals that downstream control models can consume.
This kind of model sits in a tradition of world modeling that has grown steadily over the past few years, from model based reinforcement learning to learned simulators that can predict what might happen under different sequences of actions. The difference here is that RxBrain is designed from the start as a natively embodied, multimodal world model grounded in robot sensor streams, language instructions and demonstration data, rather than as a purely abstract planner.
That grounding allows it to reason not only about what should happen in an idealized setting but also about physical risk, uncertainty and failure modes in cluttered, dynamic environments.
While public descriptions focus on architecture and role rather than exhaustive training details, the model is reported to rely on large scale multimodal embodied datasets, including text, images, video and mixed formats collected from both robotics operations and curated open source corpora. Given its size and focus, it is likely to serve most often in data center or edge server settings, providing longer horizon planning and rich guidance that can then be distilled or compressed into smaller control models running directly on robots.
VLA zero point five the action and control layer
Hy Embodied VLA zero point five completes the stack at the action layer by linking abstract goals and multimodal conditions to continuous motor commands suitable for bimanual manipulation and contact rich tasks. It is described as a vision language action model built on top of the Hy Embodied zero point five family, optimized for the core loop of real world robotics described as understand, reason and act.
The architecture again leverages the Mixture of Transformers design and embodied specific training strategies that were first developed for the Hy Embodied zero point five suite. On the data side, Tencent highlights a high precision UMI acquisition system that can capture sub millimeter level human demonstration trajectories, which are then used to build a teaching dataset exceeding ten thousand hours.
That data is combined with large volumes of self collected first person robot operation streams and open source embodied data, creating a mixed corpus that covers both human and robot perspectives. The resulting model is intended to run close to the robot’s actuators, sustaining control rates on the order of hundreds of hertz while supporting error correction, rerouting and recovery behaviors needed in unstructured environments.
The broader Hy Embodied zero point five series includes a compact Mixture of Transformers model with four billion total parameters and two billion activated parameters aimed at real time edge deployment, as well as a mixture of experts variant with a much larger four hundred seven billion total parameters and thirty two billion activated parameters for extreme reasoning performance.
Through techniques such as rejection sampling fine tuning, reinforcement learning and online distillation, Tencent reports that the smaller models inherit chain of thought style reasoning capabilities from the larger ones while maintaining fast inference. That pipeline helps bridge the gap between cloud level cognition and the tight latency budgets of robot control.
Why opening this stack matters
From an ecosystem perspective, the most significant part of the Tairos announcement is not just technical performance but the choice to open source three coordinated embodied models at once and present them as building blocks for others. Tencent’s official communications emphasize that Hy Embodied VLM one point zero, RxBrain one point zero and VLA zero point five are part of a full stack embodied intelligence solution that spans perception, cognition and action across different types of robots.
By providing modular access to models, tools and data services, Tairos is meant to lower the barrier for both academic labs and industrial teams that want to experiment with embodied agents without replicating years of internal model development.
Historically, robotics platforms such as ROS made it easier to share code and drivers, but they did not come with powerful, pretrained models for perception and planning. Likewise, open source machine learning frameworks enabled model building, yet the best performing models for many tasks remained closed or required prohibitive resources.
In that context, releasing embodied foundation models that already achieve competitive or frontier level performance on diverse benchmarks and tasks is a meaningful departure from the usual pattern of tightly held proprietary stacks.
At the same time, open models do not automatically solve everything. Embodied intelligence still demands careful system integration, robust safety engineering, and significant investment in hardware, simulation and data collection. Many potential adopters will need to adapt these models to their own sensors, actuation and tasks, and will have to validate them under stringent reliability and safety constraints.
Licensing terms, governance mechanisms and continued evaluation on real robots will all influence how much the broader community can rely on Tairos as a trusted foundation over the long term.
Implications for technology, business and society
Technologically, the Tairos stack accelerates a trend toward treating robots as general purpose embodied agents rather than pre programmed machines. With a perception model that can interpret affordances and spatial relations, a world model that can imagine futures and reason about risks, and an action model that can execute continuous control at high rates, developers gain a template for building agents that can adapt to new tasks with less manual engineering.
That shifts the emphasis from writing detailed control scripts toward designing goals, constraints and interfaces, much as large language models shifted software development toward prompt and workflow design.
For businesses, this opens new possibilities in logistics, manufacturing, warehousing, inspection and service robotics. Companies that previously saw advanced robotics as out of reach may now be able to prototype embodied agents using Tairos models and then specialize them for specific environments and workflows. Tencent’s focus on efficiency and edge deployment also matters for industries where connectivity is limited or latency constraints are strict, such as in factory floors or mobile field robots.
Societally, more capable and widely available embodied intelligence raises both opportunities and risks. On the positive side, robots that can safely navigate complex spaces, manipulate objects with fine control, and adapt to changing conditions can assist with dangerous or repetitive tasks, support care work and accessibility, and extend human capabilities in construction, agriculture and disaster response.
On the risk side, widespread deployment of embodied agents could reshape labor markets, concentrate power in organizations that control key infrastructure, and create new safety and security challenges if systems fail or are misused.
These models also heighten the need for norms around physical AI behavior. Questions about how robots should prioritize human instructions, how to handle conflicting goals in shared spaces, and how to ensure that embodied agents respect privacy and autonomy will become more pressing as the technical barriers to deployment fall. While Tairos provides a powerful technical foundation, it does not by itself resolve the ethical and governance issues that come with putting intelligent agents into the physical world.
How this fits into the embodied AI landscape
Compared with earlier embodied efforts that focused on single models or narrow task suites, Tencent’s approach is notable for its layered architecture and explicit separation of perception, cognition and action. Many projects have explored robot skill learning using vision language models that directly output low level actions from images and text.
The Tairos stack instead reflects a system level view, in which the perception model builds rich world state, the world model plans in that state space, and the action model focuses on translating plans into safe, controllable motor signals.
This modular design offers practical benefits. It allows teams to swap or fine tune individual layers without retraining the entire stack, and enables different deployment patterns such as running perception and action on the robot while hosting world modeling in the cloud.
It also encourages more rigorous evaluation and benchmarking at each level, which is important for trustworthiness and safety. If the field moves toward common embodied benchmarks and shared stacks, it becomes easier to compare approaches, identify failure modes and incorporate best practices across organizations.
At the same time, Tairos is entering a landscape that already includes strong work from other players in embodied intelligence and robot learning. Progress will likely depend not on any single stack but on how well different communities can share data, evaluation suites and lessons learned.
Open models like Hy Embodied VLM one point zero, RxBrain one point zero and VLA zero point five can serve as reference baselines that complement other systems and help anchor that shared conversation.
Takeaways and what to watch next
The key takeaway is that embodied intelligence is starting to look less like a collection of isolated demos and more like an emerging platform layer. By open sourcing a coordinated trio of perception, cognition and action models, Tencent is betting that the next wave of robotics innovation will be built on shared embodied foundations rather than entirely bespoke stacks.
Several things are worth watching in the coming months. First, how quickly researchers and developers outside Tencent adopt Tairos and report results on their own robots and tasks.
Second, whether the models continue to improve through community fine tuning, distilled variants and new data, particularly in safety critical applications.
Third, how industry and regulators respond to the prospect of widely available embodied foundation models that can control physical systems.
If those pieces come together, the Tairos release may be remembered as one of the inflection points where embodied AI moved from lab curiosity toward widely shared infrastructure, in much the same way that early open language and vision models catalyzed whole ecosystems around them.
Conclusion
Tencent is turning its embodied intelligence work into shared infrastructure for robotics at a moment when the field is finally moving from demos to deployment in factories logistics and service environments worldwide. By open sourcing a full stack of robot oriented foundation models Tencent is signaling that it wants to power a broad ecosystem of hardware makers and developers rather than compete only through single flagship robots.
From chatbots to machines that can see think and act
Over the past decade most consumer facing artificial intelligence has revolved around language models and recommendation systems while robotics lagged behind because it was harder to connect perception planning and physical control in the messy real world. That balance is starting to shift as large tech companies treat embodied intelligence as the next platform after chatbots and cloud models.
Tencent is one of the firms leading this pivot. It has been building a portfolio of robotics agents and supporting models including navigation and gripper controllers as well as vision language systems designed for direct interaction with physical environments. In parallel robotics startups such as Zeroth have begun integrating Tencent frameworks like the OpenClaw agent into humanoid platforms so that language instructions can be translated into movements on real hardware at scale. This combination of cloud intelligence and embodied control sets the stage for the latest release of Tencent’s embodied foundation models.
What exactly Tencent has opened
At the core of the announcement is a trio of embodied foundation models that correspond to the classic robotics loop of seeing imagining and acting. These are Hy Embodied VLM 1.0 Hy Embodied RxBrain 1.0 and Hy Embodied VLA 0.5 all released and open sourced around the World Artificial Intelligence Conference in 2026.
Hy Embodied VLM 1.0 is a next generation vision language model focused on understanding scenes and objects in a way that is useful for robots including recognizing what objects are for detecting changes in the environment and reasoning about spatial layout. In evaluations covering thirty seven tasks it significantly outperformed models of comparable scale while remaining relatively lightweight. Tencent reports that VLM 1.0 achieves performance close to its previous flagship A32B perception model while using roughly one tenth of the computation through an A3B configuration which makes it realistic to deploy directly on robot hardware rather than only in the cloud.
Hy Embodied RxBrain 1.0 functions as a world cognition model that unifies reasoning and imagination over visual states. It is designed to take the perception outputs from VLM and generate plans hypotheses and predicted consequences in a way that resembles an internal world model rather than a simple policy network.
Hy Embodied VLA 0.5 closes the loop by turning high level goals and language instructions into continuous motor commands for robots. It integrates vision language and action in a single model and is capable of running control at rates between roughly five hundred and one thousand hertz so that a robot can execute fine grained movements and react quickly to contact and balance events. To support this model Tencent has collected more than ten thousand hours of high precision human demonstration data using a sub millimeter accuracy UMI teleoperation system which gives the model a rich library of real manipulation trajectories in cluttered environments.
This trio does not exist in isolation. Earlier in 2026 Tencent’s Robotics X Lab and Hunyuan team released the HY Embodied 0.5 suite a family of embodied foundation models tailored to real world agents with a Mixture of Transformers architecture that separates vision and language processing while keeping both strong. The suite includes an efficient MoT 2B variant optimized for real time response at the edge and a larger MoE 32B configuration that activates around thirty two billion parameters for more complex reasoning tasks. HY Embodied 0.5 and the enhanced HY Embodied 0.5 X version have been open sourced on major model hubs giving researchers and startups access to the same building blocks Tencent uses internally.
Together these releases form what Tencent describes as a full stack embodied intelligence solution that spans data collection training and deployment for robots of many forms.
A three layer robot brain and always online agents
Technically the most interesting aspect of Tencent’s approach is the explicit brain like architecture and agent framework it is building around these models. The company presents the Hy Embodied series as a three layer matrix in which VLM handles perception RxBrain takes care of cognitive planning and imagination and VLA drives continuous control on hardware.
On top of these models Tencent unveiled Apexio a continuously online embodied agent that runs different capabilities at different frequencies so it can combine deep deliberation with fast reflexes. The top cognition layer wakes up when needed for heavier reasoning while the middle perception and action layer runs at around fifteen hertz to ingest multimodal information and adjust behaviour. At the lowest level an execution system operates at even higher frequency reacting instantly to collisions or loss of balance in a manner similar to human reflex arcs.
Tencent also introduced the TairosAgent framework which organizes perception reaction cognitive decision making and exploration into three natively embodied layers all tuned for real world tasks. TairosAgent maintains three kinds of memory environment memory self memory and task memory which helps robots build persistent understanding of their surroundings themselves and the missions they are asked to perform.
This type of layered design reflects lessons from both classical robotics and modern large model architectures. It acknowledges that no single model running at one speed can simultaneously deliver global reasoning local situational awareness and millisecond level motor control.
Strategic positioning: infrastructure for the robotics ecosystem
By opening these embodied models Tencent is clearly positioning itself more as a foundational infrastructure provider than as a pure hardware manufacturer or single robot vendor. The models are modular and cloud ready but they are also sufficiently efficient to run at the edge on robot compute in many scenarios.
Placing VLM RxBrain and VLA in the public domain through open source licenses and major model platforms lowers the entry barrier for robotics startups systems integrators and industrial users who do not have the budget or time to train such models from scratch. Smaller teams can plug these models into their own stacks adapt them to specific robot morphologies and focus their efforts on integration safety and product design.
At the same time open sourcing invites global scrutiny and contribution. Independent researchers can benchmark the models against alternative embodied systems examine their failure modes and submit improvements or specialized variants for domains such as warehouse automation surgical robotics or household assistance. This external pressure tends to uncover blind spots faster and can lead to more robust policies in the long run than purely internal development cycles.
Commercially the move fits with Tencent’s broader strategy in cloud gaming social platforms and digital infrastructure where the company often provides core capabilities that partners and developers build on. In embodied intelligence the company now offers perception planning and control as composable services that hardware manufacturers and robotics platforms can adopt much like they adopt speech recognition or payment systems today.
Comparison with earlier embodied efforts
Earlier embodied projects from multiple labs typically focused on narrow pieces of the problem such as grasping policies navigation in constrained environments or simulator trained reinforcement learning agents for specific tasks. Many of these systems struggled when moved onto real hardware because perception was brittle policies did not generalize and control loops were not tuned for the latency and noise of real sensors and actuators.
Tencent’s Hy Embodied suite attempts to tackle these gaps directly. The perception model is trained across diverse tasks and designed to understand function and change in scenes not just static recognition. The world cognition component explicitly merges reasoning and imagination which is closer to the predictive world models many robotics researchers have argued are necessary for long horizon tasks. The action model runs at very high control frequency and is trained on a large corpus of precise human teleoperation data which improves its ability to reproduce nuanced manipulation strategies rather than coarse scripted motions.
The scale of training is also notable. HY Embodied 0.5 models are trained on more than one hundred million samples and incorporate sophisticated mixture of experts routing to keep computation manageable while still benefiting from large parameter counts when needed. This is a different design choice from earlier monolithic policies and should make it easier to deploy on varied hardware with different compute budgets.
Opportunities and risks for technology and industry
If these embodied models work as advertised the immediate opportunity is faster time to market for robots in logistics manufacturing retail and even consumer settings. Companies can reuse Tencent’s perception and action layers while customizing higher level logic and safety constraints to fit local regulations and brand requirements. This could accelerate the move from pilot projects to production deployments.
For robot makers in China the models create an alternative to purely Western stacks and may dovetail with domestic efforts to standardize embodied intelligence interfaces and safety guidelines. For international partners the fact that the models are open and well documented could make them attractive as neutral infrastructure components rather than vendor locked black boxes.
However there are real risks. Embodied systems running at high frequency with rich autonomy can cause physical harm in workplaces and public spaces if they misinterpret scenes or plan incorrect actions. Open source models can be forked and modified in ways that degrade safety especially if cost pressured manufacturers try to strip away guardrails or validation procedures. There is also a risk of overreliance on a single stack if too many robots depend on one family of models and a systemic flaw is discovered.
Governance therefore becomes a central question. The field will need clear testing protocols transparency around training data sources and ways to certify updates so that industrial users know what level of reliability to expect when they upgrade a perception module or control policy. Regulatory bodies and standards organizations will likely look closely at how companies like Tencent document distribution shifts failure cases and mitigations across the embodied suite.
What to watch next
There are three practical indicators to watch in the coming years. First adoption and depth of integration which will show whether robot makers and platforms treat Tencent’s models as default infrastructure or merely as one option among many. Second the emergence of community extensions and forks which will reveal how vibrant the developer ecosystem around these models becomes. Third real safety and reliability metrics from deployments in factories warehouses and public service environments.
If Tencent succeeds its embodied stack could become something like an operating system for robots with vision cognition and control modules that are widely trusted and continuously updated in collaboration with the wider research community. If it falls short the field will learn that even well engineered foundation models are not enough without equally strong practices in safety validation and system integration.
Either way the decision to open these advanced embodied models marks a significant shift in how large technology companies treat robotics intelligence no longer as siloed proprietary code but as shared infrastructure that the global ecosystem can build on and critique in equal measure reddit
