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.
- The Robot Brain Is Splitting by Time
Léo Morillon, who writes about the emerging robotics stack, described a robot brain being divided by latency: “Rent the plan, not the reflex.” The premise is straightforward. Some parts of a robot’s intelligence can move to the cloud. Others physically cannot. - THANK YOU FOR YOUR ATTENTION
Trust, selective cognition and the reflexive intelligence robots need around humans - The Ultimate Bottleneck for Robotics: Trust
Robots can already see, navigate and plan. So why aren’t they everywhere?
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
- Building the infrastructure for trusted robotics: The Robot Benchmark and The Robot Range
SpatioTemporal is proud to be a Founding Partner of two new initiatives designed to help solve one of the biggest problems facing robotics and Physical AI: proving what robots can actually do in the real world. - 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.
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 →