Robots can see. Robots can plan.
But can they read the room?
SpatioTemporal is building the trust layer for Physical AI, starting with Motion Intelligence – a foundation model that understands movement and intent.
Movement is a language all humans understand.
We call it Motion Intelligence.
Movement carries information: a pedestrian hesitates. A cyclist begins to turn. A vehicle drifts towards a lane. Long before an action is complete, motion reveals our intent, and our intent colours what may happen next.
SpatioTemporal models these patterns directly, giving machines another source of intelligence for understanding the dynamic world around them.
Compressing space and time into Motion Tokens – the alphabet of movement.
Position, direction, velocity and acceleration become sequences of learned Motion Tokens, giving the model a compact representation of movement through space and time.
We model continuous movement as a discrete vocabulary of 10,000 learned motion primitives. Motion is a language, and this is its alphabet.
Motion Tokens give models a compact way to reason about how things move, how movement changes, and what those changes imply.
See 10,000 Motion Tokens in our interactive explorer →
Research is how we test the thesis
From model architecture and simulation to human behaviour and Physical AI systems, our work explores what machines need to understand before they can operate naturally around people.
24% → 2%
Near-collisions in an NVIDIA Cosmos simulation, comparing an unchanged navigation planner against the same planner augmented with Motion Intelligence.
Earlier yielding. Smoother shared-space negotiation. Less planner volatility.
Read how we performed the experiment →
News
- SpatioTemporal Named Runner-Up in Propel-AIR 2.0
SpatioTemporal was named runner-up in Propel-AIR 2.0, Australia’s AI and robotics commercialisation program led by ARM Hub and designed to help Australian technology companies build pathways into international markets. - SpatioTemporal featured on SME AI’s ‘ROI from AI’ Podcast
SpatioTemporal founder Andrew Ballard recently joined Andrew Lai and Amir Nissen from SMEC AI for a conversation about Motion Intelligence, foundation models and a different approach to building AI for the physical world. - The Data Wire: Physical AI Needs Better Signals, Not Bigger Data Pipelines
SpatioTemporal founder Andrew Ballard has been interviewed by The Data Wire for a feature exploring a growing challenge for Physical AI: how to extract the signals that matter without carrying the enormous computational burden of continuous video.
Robots that read the room.
Cars that read the road.
SpatioTemporal is building Motion Intelligence for machines operating in the human world.
Explore the technology →
Read the research →
Work with SpatioTemporal →