closing the sim-to-real gap. rapidly synthesize, iterate, and deploy neural policies using hyper-parallel physics environments and autonomous reward shaping.
monitor the convergence of complex neural architectures. direct access to low-level physics telemetry and agent-level decision logs.
SYSTEM_CAPABILITIES_01 // PHYSICS_ENGINE
ingest any morphology and execute thousands of concurrent environments with zero-copy tensor state.
high-fidelity urdf/mjcf ingestion. real-time joint articulation profiling, collision mesh validation, and full topological analysis.
gpu-accelerated physics orchestration. execute 4096+ environments in a single on-device tensor graph. zero-copy state access.
aggregate scoring across random seeds. safety-constrained evaluation with multi-angle neural rendering for visual verification.
SYSTEM_CAPABILITIES_02 // AUTONOMY_CORE
delegate complex reward engineering to an autonomous reasoning agent. optimus writes, evaluates, and iteratively improves training code at machine speed.
large-scale autonomous reasoning for complex robotics. optimus interprets physical failure modes, synthesizes optimal reward functions, and orchestrates hyper-parallel policy training with zero human oversight.
automated reward shaping via large-scale heuristic search. eliminate manual engineering.
integrated runtime interface for policy debugging. real-time signal monitoring and live logs.
SYSTEM_CAPABILITIES_03 // MLOPS_DEPLOY
scale compute globally and export strictly-versioned policies directly to edge silicon.
seamless scale to cloud compute clusters. automatic hardware provisioning with encrypted peering and job scheduling.
immutable experiment tracking. every run is a hashed branch with artifacts, configs, and metrics linked to git-tree state.
compile trained policies to onnx, torchscript, or tensorrt. ready for deployment on real edge-hardware with calibrated latency.
SYSTEM_PIPELINE // EXECUTION_LEDGER_v4
a deterministic four-stage process for bridging raw kinematic definitions to edge-optimized neural control policies.
parse urdf/mjcf models into high-performance kinematic tensors. mesh validation and mass-matrix optimization.
instantiate 4,096+ synchronized physics environments. zero-latency gradient collection across gpu threads.
optimus agent-led reward engineering. iterative policy search and heuristic optimization for high-dof tasks.
compile neural policies into deterministic execution graphs. calibrated for zero-shot edge silicon deployment.
PLATFORM_INTEGRATION // KINEMATIC_KERNEL
our underlying simulation manifold is strictly topology-agnostic. automatic differentiation of articulated rigid-body dynamics via sparse, unrolled jacobian matrices.
stable simulation of closed kinematic loops and floating-base systems. automatic resolution of mass-matrices and non-linear joint constraints.
high-throughput experience collection and value function convergence. distributed proximal policy optimization (ppo) ensures monotonic improvement across highly-dimensional action spaces.
compile neural weights into deterministic binary execution graphs. calibrated for zero-shot deployment on raw edge-silicon with sub-millisecond jitter.
SUPPORTED_MORPHOLOGIES // ATOMIC_PROFILES
UNITREE_GO2
UNITREE_H1
FRANKA_PANDA
PHOENIX_MK4
AGILITY_DIGIT
SHADOW_HAND
M300_RTK
ANYMAL_W
ABB_IRB_360
HEBI_SNAKE
ANYMAL_C
STEWART_PLAT
BLUE_ROV2
SARCOS_XOS
CRAZYFLIE
CUSTOM_URDF
entire robot states (joint positions, velocities, forces) are flattened into a single contiguous gpu buffer. policies interact with the simulation via zero-copy tensor slicing, bypassing cpu bottleneck entirely and allowing for millions of physics steps per second.
autonomous perturbation of mass matrices, friction coefficients, and actuator latency during rollout. our kernel guarantees mathematical robustness across the entire parameter distribution, ensuring high-fidelity sim-to-real weight transfer.
SYSTEM_ADVANTAGE // DIFFERENTIATION
we do not build simple simulators. iacon is a fully integrated reinforcement learning compiler designed explicitly to close the sim-to-real gap.
abandon serial cpu simulation. our entire physics and reward evaluation stack compiles directly to cuda kernels, enabling continuous exploration across thousands of concurrent environments at 500+ hz.
numerical reward hacking is inevitable. we prevent deployment failures by natively rendering multi-angle verification rollouts of the top-performing policies, allowing for visual inspection of failure modes.
simulation weights are meaningless if they cannot run on edge hardware. iacon exports statically-typed, dimension-checked execution graphs (onnx/tensorrt) calibrated for the latency limits of the target hardware.
SYSTEM_READY
the private beta is active. allocate compute nodes, synthesize your reward landscapes, and export zero-shot neural policies to edge hardware.