World Model for Robots

Introduction: Why World Models Matter in Robotics

In robotics, the concept of a world model has become central to how machines perceive, reason, and act within their environments. A world model can be thought of as an internal representation of the external world that enables a robot to predict outcomes, plan actions, and adapt to new situations. In many ways, this mirrors how humans form mental models of their surroundings. When we walk through a room, we do not process every sensory detail in real time. Instead, we rely on an internal understanding of the layout, the position of objects, and the likely consequences of our movements. This ability to anticipate and plan is what allows us to act efficiently and safely, and it is precisely what researchers aim to achieve in robotic systems through the development of world models.

For humans, world models are not simply static maps of reality. They are dynamic, predictive, and deeply tied to memory and imagination. We can recall past experiences to guide present choices and simulate possible futures before acting. For example, when deciding whether to cross a busy street, we do not rely solely on the immediate view of traffic. We draw on prior knowledge of how cars accelerate, anticipate the intentions of drivers, and mentally rehearse our own movement across the road. This capacity to combine perception with prediction allows us to navigate uncertain and changing environments with remarkable efficiency. In robotics, world models serve a similar purpose by enabling machines to go beyond reactive behavior and instead reason about what might happen next.

What Are World Models in Robotics?

In robotics, a world model can be formally defined as an internal representation that encodes the structure, dynamics, and uncertainties of the environment in which a robot operates. This representation can take many forms, from geometric maps of physical space to probabilistic models of object behavior or learned latent spaces in neural networks. The purpose of a world model is not only to describe what the robot currently perceives but also to provide a foundation for predicting how the environment might evolve in response to the robot’s actions. Without such a model, a robot is limited to purely reactive control, responding only to immediate sensory input. While this may be sufficient for simple or repetitive tasks, it becomes inadequate in complex, dynamic, or safety-critical settings where foresight and planning are essential.

Why Robots Cannot Simply Be Language Models?

Recent progress in large-scale learning has made the importance of world models even more apparent. Vision–Language–Action (VLA) models, such as PaLM-E [1] and RT-2 [2], demonstrate how a unified representation of perception, reasoning, and control can allow robots to generalize across tasks and environments. These models rely on a form of implicit world modeling: they connect visual input with symbolic language descriptions and translate that knowledge into motor actions. By grounding actions in a structured understanding of the environment, VLA systems illustrate the power of world models to bridge perception and decision-making. Without such internal representations, robots remain bound to narrow, pre-programmed routines. With them, they begin to display flexible, human-like adaptability, capable of reasoning about novel instructions and acting safely in unstructured real-world scenarios.

Gemini Robotics: VLA Model showcased at RSS 2025, translating voice commands and camera inputs into real-time motor actions.

This naturally leads to a deeper question: if language models can already generalize so effectively in the symbolic space of text, why does robotics remain such a harder problem? To understand this contrast, it is helpful to examine how language models have evolved and why their success cannot be directly translated into the physical world.

Lessons from Language Models

The success of modern LLMs is built on decades of progress in representing and predicting patterns in language. Early models such as word2vec [3] demonstrated that words could be embedded into a latent space where semantic relationships emerged naturally. Similar words clustered together, and analogies could be captured through vector arithmetic. This embedding-based view of language enabled models to move beyond surface-level text toward representations that reflected meaning and context. Later, sequence-to-sequence (seq2seq) architectures [4] extended this idea by learning to map entire input sequences into compressed latent representations, from which coherent output sequences could be generated. These advances laid the foundation for today’s transformer-based models, which operate in vast latent spaces to predict the most probable continuation of text.

The Harder Challenge of Robotics

In robotics, however, the challenge is fundamentally different. Robots must not only represent data in a latent space but also anchor those representations to the laws of physics and the constraints of embodiment. For instance, predicting the next state of a robot arm is not a matter of choosing the most probable token in a vocabulary, but of computing how torques applied at the joints will propagate through the kinematic chain under gravity, friction, and contact dynamics. The robot must account for delays, noise, and uncertainties, all while operating in real time. A misprediction here is not just a grammatical error; it could result in a collision, wasted energy, or even damage to the robot and its surroundings.

Moreover, while language models can rely on abundant, static text data scraped from the web, robots must learn from interaction data that is expensive to collect and often task-specific. Each trial consumes time, energy, and hardware wear, which severely limits the scale at which data can be gathered. Efforts to address this limitation, such as large-scale robot learning platforms [5], attempt to bridge the gap by pooling data across fleets and tasks, but the fundamental bottleneck remains. This makes the construction of world models especially valuable: by learning internal representations that capture dynamics, robots can perform imagination-based planning, predicting outcomes in simulation before executing actions in the real world.

In short, the divergence between LLMs and robotic models highlights why world models are indispensable. Where LLMs succeed by mastering the statistics of symbolic sequences, robots must integrate perception, physics, and control into a unified predictive framework. World models offer the means to connect these domains, allowing robots to reason in latent space while staying grounded in the constraints of the physical world.

Well, how do we build a world model?

This question has led to two complementary directions in research. On one hand, classical approaches build structured world models grounded in geometry, kinematics, and probabilistic mapping. On the other hand, recent advances in deep learning have enabled robots to learn latent dynamics models, allowing them to simulate possible futures in compressed representation spaces. Both approaches share the same goal: to provide a robot with the predictive capacity needed to act safely and adaptively in complex, uncertain environments.

Structured world models. Traditional robotics has long relied on models based on explicit mathematics and physics. These include geometric maps of the environment, kinematic equations describing how a robot moves, and probabilistic filters such as the Kalman filter that handle uncertainty in sensing and control. The advantage of this approach is transparency: the model is interpretable, grounded in physical principles, and often works well in structured environments. However, it can become brittle when the environment is complex or when assumptions—such as perfect sensing or rigid dynamics—no longer hold.

Learned world models. More recent approaches train neural networks to capture the underlying dynamics of the world directly from data. Instead of hand-coding every detail, the robot learns to compress its experiences into a latent representation that can be rolled forward in time. This allows the robot to perform what is sometimes called “imagination-based planning”: it can run thousands of simulated futures in its internal model to evaluate possible actions before executing them in reality. Such learned models are powerful but require a huge amount of data to train these models.

From this point onward, I will draw on my own experiences of interacting with the world and outline how I believe world models should be developed for robots.

How do world models look to humans?

For humans, a world model is not a perfect map of reality. Instead, it is a working picture of the world that we constantly update and use to guide our actions. At the most basic level, this picture helps us make fast judgments about objects, surfaces, and movements, like knowing a cup is solid, or that the floor will support us when we step forward. On top of that, we layer meaning: we learn categories, labels, and relationships, such as “this is a chair” or “that object is heavy.” At the highest level, our models include social rules and expectations, such as taking turns in a conversation or stopping at a red light. Memory ties these layers together, and imagination lets us test “what if” scenarios before acting. The result is not a flawless reflection of reality but a useful guide that helps us act effectively in the world.

Importantly, this world model is not the same for everyone. It varies with culture, upbringing, language, and personal experience. Yet coexistence does not require identical models. What matters is an overlap of enough shared understanding to cooperate and resolve disagreements. Humans maintain this overlap through shared environments, communication, imitation, and social systems such as signs, rules, and institutions. When our models diverge, we adapt: we negotiate, clarify, or rely on external aids like maps, manuals, or traffic signals. Alignment is a process, not a state.

This variability is not a weakness but a strength. Differences in how people see the world make groups better at adapting to surprise, as long as there are ways to resolve conflicts. In daily life, we continuously align with one another in three ways. First, by agreeing on what is present (“we both see the same obstacle”). Second, by agreeing on what might happen next (“the car is going to hit the obstacle”). And third, by agreeing on what should be done (“we should steer the car away to avoid collision”). These small loops of alignment happen in every conversation, team effort, and shared task.

What does this imply for robots?

Robots do not need to copy the entire complexity of human world models to be useful. What they need is compatibility: their internal models should connect to the ways humans see and coordinate in the world, and they should be able to adjust when they go wrong. The goal is not to build robots that “think like us,” but robots that can act safely, predictably, and in line with human expectations.

A practical way to imagine this is to give robots layers of understanding, similar in spirit to ours:

• Physical layer: knowing where objects and obstacles are, and how movement works.

• Functional layer: recognizing what things can be used for, such as a handle for pulling or a shelf for placing items.

• Social layer: following conventions, like yielding space in a hallway or waiting for a handover.

• Imagination layer: being able to run through possible futures internally before making a move.

• Correction layer: noticing when things do not go as expected and adjusting safely.

  
  

Answering the core worry

We do not fully understand how human world models work, and we do not need to in order to build effective robots. Humans themselves do not share identical models, yet we coordinate successfully because we maintain overlap and rely on tools like language, rules, and shared spaces to smooth differences. Robots can follow the same path: build modest but useful models, remain open to correction, and always ground predictions in physics and safety. The goal is not to copy the human mind but to create systems that act predictively, adapt safely, and work in harmony with human intent in the worlds we share.

References

1. Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Wahid, A., ... & Florence, P. (2023). Palm-e: An embodied multimodal language model.

2. Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., ... & Han, K. (2023, December). Rt-2: Vision-language-action models transfer web knowledge to robotic control. In Conference on Robot Learning (pp. 2165-2183). PMLR.

3. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781.

4. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.

5. Kalashnikov, D., Irpan, A., et al. (2018). QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. arXiv:1806.10293.