Demos

A Unitree G1 walks end-to-end
through AEROS.

Runtime schedules. ECM dispatches. Governance approves. Embodiment executes. Four layers, one closed loop — the same code that ships in the open-source repo.

30s narrated walk-through. Captions land on each chain step: Runtime → ECM → Governance → Embodiment. Live HUD telemetry uses the actual data shapes the bridge emits. Sim G1 in MuJoCo · seamless loop.

What's running

What you're seeing

Robot Unitree G1 humanoid (MuJoCo simulation)
Policy ONNX bipedal locomotion
Distance & speed Pelvis advances ≥0.4 m over 1.5 s per NavigateECM dispatch
Validation Live integration test boots a real SimulatedUnitreeG1Robot + NavigateECM and asserts pelvis advances ≥0.3 m under the g1_onnx_locomotion policy
License posture No AGPL third-party dependencies in the resolved graph — the make license-check gate keeps the dual-licensing story clean
The chain

From scheduler to joint torque

The same call path that runs in this sim is what a real-robot bridge process executes. Every layer is named, versioned, and traceable to source files in the open repo.

1

Runtime — lifecycle & scheduling

Bridge process initializes, loads the IdentityManifest, attaches the persona engine + Layer 2 consolidator + DreamScheduler. Lifecycle is init → start → pause → resume → stop → restart.

bridge/app/main.py
↓
2

ECM — capability dispatch

NavigateECM v2 receives a structured intent from the planner; the v1/v2 dispatcher pattern picks the right handler with stable fallback_reason if v2 raises. v2 calls into the G1 robot's apply_twist.

bridge/app/ecm/navigate.py
↓
3

Governance — policy & audit

Capability admission, policy DSL check, watcher registration. Persona engine evaluates the resulting PersonaAdaptiveEvent stream; tighten events emit through the Ed25519 audit chain. Outbox pattern guarantees at-least-once delivery.

src/aeros/runtime/persona_emit.py · semantic_consolidator.py
↓
4

Embodiment — ONNX policy + joint command

An ONNX-controller class receives a TwistCommand; the vendored G1 locomotion policy (~880 KB) computes joint torques; MuJoCo steps the simulation. The same controller class plugs into the real-robot bridge on the hardware track.

Reproduce

Run the same chain locally

Three commands. The G1 walks under the same NavigateECM chain the video shows. The narrated demo drivers follow with the public benchmark harness. 

git clone https://github.com/s20sc/aeros-core.git
cd aeros-core && make setup
.venv/bin/pytest tests/sim/test_navigate_g1_policy_integration.py -v
# live integration test: real SimulatedUnitreeG1Robot + NavigateECM,
# asserts pelvis advances ≥0.3 m under the ONNX locomotion policy

What's next

Hardware track

Real-robot bridge

Migrate this exact chain off the MuJoCo sim onto a real Unitree G1. Same NavigateECM, same policy, same governance — different embodiment driver.

Roadmap

Manipulation policies

Grasp-and-place ECM v2 with shadow-mode rollout. Locomotion is the proof; manipulation is the next surface.

Changelog →