Moravec's Paradox says that machines can do some amazing things that we humans find hard - and yet the things we learn as children, machines still struggle with:

How to avoid collisions.
How to read hesitation.
How to move with others.
How to sense what may happen next.

They're some of the fundamental human instincts that we use every time we go out into the world – they're the things that keep us safe.

Yet they remain some of the hardest things for Physical AI – robots and self-driving cars – to learn.

Perception tells a machine what is there.
Planning tells it what to do.

What's missing is understanding movement, intent and consequence – those instincts that keep humans safe, transferred into the realm of robotics and AI.

At SpatioTemporal, we're building a family of intelligences to address that gap:

SPATIAL – Motion, intent and human awareness.
TEMPORAL – Predictions, causality and future world state.

We believe these are the missing middle stack Physical AI needs to interact safely around humans.

We began with the most universal signal: movement.

We've built a foundation model that treats motion as a language, and intent as something that can be inferred. Not from pixels, but from how things move through space and time.

Our model can help infer signals like hesitation, distraction, instability, and changing intent - often before explicit actions occur.

Allowing robots to read the room, and cars to read the road.

In NVIDIA Cosmos simulations designed to force close interactions between robots and humans, we saw near-collisions fall by over 90% when we introduced our Motion Intelligence model. We saw:

Earlier yielding
Smoother shared-space negotiation
Less planner volatility.

Showing that our Motion Intelligence model, and the wider range of Spatial and Temporal Intelligences we're developing, may become foundational layers in emerging Physical AI stacks.

And just as important: we generated richer human behaviours, helping to close the sim-to-real gap in Physical AI by varying perhaps the most underused factor of all – human behaviour.

The new era of Physical AI will need more than perception and planning. It will need machines that understand humans enough to be trusted to fit into the human world.

That's the frontier we're exploring – from Motion Intelligence to a broader family of SpatioTemporal Intelligences.

If this is a world you need to understand, connect on LinkedIn.