Blog Viewer

Imagine a human-shaped home assistant (let’s call him Jed) who doesn’t just see your living room – he understands it. Jed knows the throw rug tends to bunch up near the hallway at night. He predicts that after you take your evening meds, you’ll likely head to the kitchen for water. He can quietly suggest turning on a light, or do it for you, smooth the rug and position himself to offer a steadying hand.

That sense of anticipating what happens next is the promise of a new kind of AI architecture known as world models. They give machines an internal understanding of the space around them so they can plan, reason and act with foresight rather than react more like today’s clever autocomplete.

Mental Models

In technical terms, a world model is a compact, internal map of physical reality that lets an AI-powered robot simulate the consequences of actions before taking them. We humans do this constantly as we create mental models. For instance, we imagine crossing the street, forecast where the cars will be, and adjust.

Unlike a large language model (LLM) that reacts to inputs without a deep understanding of context, a world model learns how the environment changes when something happens, then rolls that state forward to anticipate how to react. The practical effect is remarkable: Whether it’s a robot grasping a mug or a car merging onto I-95, the system can mentally try a few options and pick the safest, most effective one.

How We Got Here

The concept has roots in the 1940s, when psychologists Kenneth Craik and Edward Tolman argued that minds form internal models or cognitive maps to navigate the world. Decades later, reinforcement learning researchers formalized planning with learned models. For instance Richard Sutton’s “Dyna” idea in the 1990s proposed that agents should both learn from real experience and rehearse with an internal simulator.

The big leap came when neural networks got good at compressing high-dimensional sensory data into a latent space. And in 2018, a paper literally titled World Models showed how an AI could learn a visual world in that compressed space, “dream” through futures and train a controller using dreams. Follow-on methods like PlaNet and Dreamer improved the data efficiency and stability of learning by imagination. DeepMind’s MuZero took a different but related tack: learn just the parts of the model that matter for planning and still achieve superhuman game play … without being handed the rules ;-)

Meanwhile, computer vision scaled thanks to Fei-Fei Li’s foundational work which catalyzed modern visual recognition. Her later emphasis on human-centered and embodied AI steered perception away from labels toward tasks and environments people actually care about in their daily routines, like kitchens and bathrooms. And that orientation is exactly where world models can create real impact for older adults.

Industry Leaders

Tesla’s autonomy program is a high-profile example of world-model thinking in the wild. Their tech builds a persistent 3D understanding of the driving scene from cameras – occupancy grids, lanes, moving objects, etc. –  and increasingly relies on end-to-end neural policies to plan movement through that world. You might call this a more pragmatic world model: It ingests pixels, maintains a working map of reality and uses it to choose the next maneuver.

OpenAI (maker of ChatGPT) has framed video generation as a path to world simulation, suggesting that large video models can serve as scaffolding for agents to learn physics and cause-and-effect before acting.

NVIDIA, not to be left out, is pushing hard on generalist robotics. Think: tooling, simulators and foundation models for humanoids so a diversity of robot types can learn common skills within shared world models. Finally, a wave of robotics startups (Figure, 1X, Unitree and others) are racing to fuse perception, language and action into embodied agents that can operate with acuity in messy real spaces.

If we tie all these together, we begin to see the shape of a near future: AI systems that don’t just classify images or chat but maintain an internal “movie” of what’s happening around them, imagine what could happen and intervene usefully.

What’s Next

For organizations designing products and services for older adults, the “imagine first, act second” method found in world models is not a risk-enhancer but a safety feature.

• Falls prevention, proactively: A home assistant that can predict likely paths and spot hazards (rugs, cords, pets underfoot) can nudge lighting, move obstacles, or alert a caregiver before a risky situation occurs. Planning in advance, not reacting after a fall, is a key difference.
• Mobility and transfers: Moving from bed to chair, or navigating stairs, requires anticipating weight shifts and balance. A world-model-aware device can position itself to assist without getting in the way and know when to stop and ask for human help.
• Medication and daily routines: Adherence is context-dependent. If the system understands where the person is in the home, what they’re doing, and what usually happens next, it can time a reminder or deliver a pillbox when it’s actually useful.
• Home setup, continuously improved: With a world model of the environment, the system can test changes in simulation – grab-bar placement, night-light positioning, furniture layout –  before anyone drills holes or moves heavy items. Kind of an evolution of our own HomeFit guide.
• Caregiver relief: The assistant can translate that internal movie into human-readable summaries (“she’s in the den; likely to go to the kitchen next… path is clear”) and escalate only when it matters. That’s less noise, more signal and a lighter cognitive load.
  

So effectively, world models will let us build assistants that are attentive, not intrusive; predictive and not paternalistic.

The Caveats

World models are magical but not quite magic. Our homes are idiosyncratic – lighting changes, clutter gathers, pets do what pets do.

Put simply, our humanoid friends will face surprises. And the bridge from simulation to reality is still rickety when it comes to things like fluids and fabrics … particularly when finer manipulation is required.

But progress is compounding. The leap from “recognize a mug” to “clear a safe path to the sink and put the mug on the drying rack” is exactly the kind of leap world models enable.

So when will Jed create a model of your world and give you the Life of Riley? It may be sooner than you think.


Rick Robinson is VP & GM of the AgeTech Collaborative™ from AARP and produces a newsletter on AI called aislop.ai.