The Charlot Lab.
The Dean's research group works on the foundations of trustworthy embodied AI — systems that are provable, physically grounded, and cheap enough to run on the device — across two threads: Interface Engineering and Swap-2C Constrained AI.
Led by David Jean Charlot, Dean of Physical AI.
Two threads.
What the lab works on.
Clean seams between parts
The engineering of the interfaces between the pieces of an embodied system — hardware, software, sensors, and subsystems — so modular systems compose without bespoke glue.
Provable, grounded, cheap to run
AI grounded in physics and math rather than language: closed-form primitives, controllers traced to a Lyapunov function, and a picojoule energy receipt on every call — so behavior is provable and the power cost is known before deployment. It runs in a WebGPU browser in under 400 KB.
Live work.
The lab's research runs in the open, as working sites you can use.
Math-Ground AI
The Swap-2C runtime — physics- and math-grounded primitives, closed-form control, and energy accounting.
Visit ↗Pattern-Lang
A lawful route to AGI — recognizing patterns by naming, composing, and verifying them over a small finite lexicon, instead of learning them from billions of examples.
Visit ↗Play Dimension
A strategy game whose mechanics map to ideas from AI and neuroscience.
Visit ↗Build this with us.
The lab takes on students through internships, and works with investigators across the Institute.