Research
Our research addresses the gap between systems that can reason about the world and systems that can operate reliably within it.
We study how multimodal models, physical agents, efficient computing systems and human interfaces can work together under changing conditions and incomplete information.
Five questions
How can models build useful representations of complex physical environments?
How can systems reason over spatial and temporal change?
How can machines learn from demonstrations, interventions and failures?
How can advanced models operate efficiently on constrained edge hardware?
How should physical-intelligence systems be evaluated and governed?
Programs
Lifecycle and evidence status shown on every record.
Multimodal models that represent crops, terrain, field conditions and change over time using ground imagery, video, geospatial information, weather, metadata and human observations.
Learning systems that connect perception and reasoning to physical action, beginning with agricultural machines operating in unstructured outdoor environments.
Efficient models and runtimes designed to operate locally where bandwidth, latency, power, privacy or reliability make continuous cloud access unsuitable.
Speech and language systems that support natural interaction with scientific and physical-intelligence systems, beginning with Twi, Ghanaian English and real-world code-switching.
Evaluation methods for determining whether intelligent systems remain reliable, calibrated and safe outside controlled demonstrations.
Field methodology
Physical intelligence cannot be evaluated from a single curated image or demonstration. Our intended methodology connects environment, task, observation, model behavior, human intervention, physical action and subsequent outcome.
Public claims will be tied to a named protocol, version, evidence basis and stated limitations.
Explore safety and evaluation