NexSim · Simulation
Where Nexa learns before it acts.
Physiologically accurate simulation of a real biofidelic body, fast enough to train at scale.
Why now
Simulation crossed a computational threshold
Physiologically accurate musculoskeletal simulation — built on real biomechanical models rather than rigid-body approximations — used to be far too expensive for reinforcement learning at scale. It is not any more. A high-fidelity body can be modelled and trained in simulation before any hardware exists.
Why it has to be biofidelic
A rigid-body simulator trains the wrong intuitions
Train on rigid-body data at scale and you get something impressive: real, hard-won intuitions about force and balance. Just not ours — intuitions about being that machine. More data does not fix it, because the distortion is systematic rather than noise. It only makes the system more confident in being wrong.
Simulating a body that pulls rather than pushes, that carries redundant opposing muscle groups and distributed sensing, is the only way the training transfers to one.
The loop
Where NexSim sits
Four products, one loop. NexSim is the step between capturing what a person did and running a model inside a body.
- 01
Build Nexa
The most biofidelic android — the only kind whose competence can be measured against a person's directly, because it is the only one under the same constraints.
- 02
Capture the Human Foundation Dataset with NexCap
Not what someone did, but how a body learned to do it.
- 03
Train Nexus in NexSim
The Human Foundation Model, trained against a physiologically accurate body rather than a rigid approximation of one.
- 04
Run Nexus inside Nexa
And measure everything against the Biofidelity Benchmark.
The argument in full
Why a body that matches the human mechanism — rather than merely resembling it — is the precondition for general intelligence, not an application of it.