the sim-to-real gap is fundamentally an i/o problem. iacon eliminates temporal drift by enforcing hardware-level timestamping across all physical buses, fusing high-bandwidth proprioceptive and spatial data directly into gpu memory.
robotic perception is typically fractured across asynchronous software nodes. vision pipelines process frames at 30hz, while motor controllers demand 1000hz torque commands. this temporal desynchronization injects phase delay, destroying the causal structure required by transformer-based policies.
"we bypass traditional middleware. the iacon stack writes raw encoded bytes directly to pre-allocated gpu memory, synchronizing modalities via strict hardware interrupt scaling."
by enforcing strict temporal alignment, we guarantee that every tensor passed to the large behavior model represents a perfectly coherent snapshot of continuous reality.
SYSTEM_ARCHITECTURE // SENSOR_FUSION
our underlying hardware architecture is topology-agnostic. automatic synchronization of high-dimensional rigid-body dynamics via sparse, zero-copy buffer routing.
bypassing the cpu entirely. we route gmsl2 camera feeds and ethercat cyclic payloads directly into tensor cores via remote direct memory access (rdma) over pcie gen 5.
every motor on the morphology acts as a sensor. we extract 1000hz torque measurements, phase currents, and absolute encoder positions to reconstruct external contact forces implicitly.
sensors physically shift during highly dynamic locomotion. our kernel runs a background continuous calibration thread, estimating shifting extrinsic parameters between the imu and optical frames.
transformer architectures mandate strictly ordered sequential data. we map the infinite continuous state-space of the physical world into discrete, quantifiable tokens ready for inference.
c++ drivers write packet payloads directly to pinned gpu memory regions, eliminating cpu cache misses.
low-frequency camera frames are interpolated against high-frequency imu logs using hardware time-stamps.
spatial point-clouds are passed through sparse convolutional encoders, mapping physical geometry into dense tensors.
aligned latent vectors are concatenated with proprioceptive tokens, generating the context window for the lbm.
INIT_DEPLOYMENT
gain access to our hardware evaluation kits. test the deterministic latency and high-bandwidth sensor fusion capabilities of the iacon stack directly on your own robotic morphologies.