— Physicore insights
PHYSICAL AI, IN THE REAL WORLD.
Research and perspectives on the data, environments and systems shaping physical intelligence.
WHAT THE DATA ACTUALLY CONTAINS.
We reviewed every run in the public sample of DROID, the manipulation dataset behind flagship robot models, and recorded what it contains.
Read →WHAT GOOD ROBOT DEPLOYMENT LOOKS LIKE.
Reliability is one input among several. What to establish about a robot system before committing capital to a rollout.
Read →A BENCH IS NOT A BUILDING.
Robot policies are evaluated on controlled test rigs and deployed into places nothing like them. That gap is where reliability is lost.
Read →THE REAL WORLD WAS NEVER WRITTEN DOWN.
The data that built language models does not exist for the physical world. It has to be created.
Read →THE LAB IS NOT THE WORLD.
A policy that scores ninety-five percent on the bench can fall to sixty on a real floor. The world it was raised on was too small.
Read →RECORDING IS THE EASY PART.
Anyone can film a task. The value is in everything that happens between raw footage and data a model can actually learn from, and almost no one does that part well.
Read →THE DATA NOBODY IS COLLECTING YET.
Pre-training is the land grab everyone can see. The harder, stickier value sits in the data that matches deployment, and almost no one is built to collect it.
Read →DIVERSITY IS NOT A COUNTRY COUNT.
Geography matters only when it changes what a model has to survive. Diversity should be measured in conditions, not in pins on a map.
Read →THE TELEOPERATION CEILING.
Teleoperation is the gold standard for robot data and the hardest to scale. Resolving that tension, rather than ignoring it, is where the real value sits.
Read →THE EMBODIMENT GAP.
Data collected on one robot does not transfer cleanly to another. The way a capture programme handles that fact decides how much of its data survives a change of hardware.
Read →BEYOND PASS AND FAIL.
A model that succeeds is not the same as a model you understand. Evaluation that explains how and where a policy breaks is its own discipline, and most of the field has not built it.
Read →PROVENANCE DECIDES WHAT YOU CAN BUILD ON.
As physical AI moves into homes, hospitals, and factories, the question stops being how much data a model was trained on. It becomes whether anyone can prove where that data came from.
Read →