Unitree G1 Tier 1 — MuJoCo loco + real x402 + R11 evidence

.github/workflows/unitree-g1-bridge.yml

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+name: unitree-g1 Tier 1 bridge
+
+on:
+  workflow_dispatch:
+  push:
+    branches: [unitree-g1-tier-1]
+    paths:
+      - "bridge/unitree/g1/**"
+      - ".github/workflows/unitree-g1-bridge.yml"
+  pull_request:
+    paths:
+      - "bridge/unitree/g1/**"
+      - ".github/workflows/unitree-g1-bridge.yml"
+
+jobs:
+  syntax:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - run: python -m compileall -q bridge/unitree/g1/mujoco_loco
+
+  test:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - run: pip install -r bridge/unitree/g1/mujoco_loco/requirements.txt
+      - run: pytest -q bridge/unitree/g1/mujoco_loco/tests/
+
+  sim2sim:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - run: pip install -r bridge/unitree/g1/mujoco_loco/requirements.txt
+      - run: pytest -q bridge/unitree/g1/mujoco_loco/tests/test_sim2sim.py
+
+  x402-gate:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - run: pip install -r bridge/unitree/g1/mujoco_loco/requirements.txt
+      - run: pytest -q bridge/unitree/g1/mujoco_loco/tests/test_x402.py bridge/unitree/g1/mujoco_loco/tests/test_payment_gate.py bridge/unitree/g1/mujoco_loco/tests/test_unitree_g1_payment_gate.py
+
+  evidence:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - run: pip install -r bridge/unitree/g1/mujoco_loco/requirements.txt
+      - run: python -m flow.demo --all
+        working-directory: bridge/unitree/g1/mujoco_loco
+      - run: python docs/evidence/render_evidence.py
+        working-directory: bridge/unitree/g1/mujoco_loco

bridge/unitree/g1/mujoco_loco/README.md

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+# unitree-g1 — RoboPay Tier 1 bridge (Simulator Skill Execution)
+
+A paid `move_forward` / `navigate_obstacle` / `stop` skill executed by **real
+physics**, driven over **Zenoh**, paid with **x402**, and settled **only when
+the robot actually succeeded**.
+
+| | |
+|---|---|
+| robotId | `unitree-g1` |
+| profileId | `laok.unitree-g1-arm-001.loco.v1` |
+| skills | `move_forward`, `navigate_obstacle`, `stop` |
+| engines | MuJoCo (primary) + PyBullet (sim-to-sim) |
+| transport | Zenoh — `robot/tunnel/action` / `robot/tunnel/result` |
+| scope | **simulation only** — CPU, headless, no GPU, no ROS, no hardware |
+
+> **Scope statement (criterion #6).** This bridge never drives physical
+> hardware. There is no motor driver, no teleop channel and no hardware SDK in
+> the dependency list. Every action runs inside a physics engine in-process.
+
+---
+
+## 1. Quick start (< 5 minutes)
+
+```bash
+cd bridge/unitree-g1
+python -m venv .venv && . .venv/bin/activate      # Windows: .venv\Scripts\activate
+pip install -r requirements.txt
+
+pytest -q                                          # full test suite
+python -m flow.demo --all                          # the paid flow, all scenes
+```
+
+`requirements.txt` is CPU-only. MuJoCo and PyBullet both ship manylinux wheels,
+so there is nothing to compile on `ubuntu-22.04` (the CI reference platform).
+
+> **Windows note.** `zenoh` and `pybullet` publish no Windows wheels. On Windows
+> the demo runs over the loopback transport with MuJoCo — same envelopes, same
+> topics, same payment path. Use Linux (or the CI workflow) for the real Zenoh
+> session and the PyBullet cross-check.
+
+## 2. What the demo prints
+
+```
+ scene                   status     reason       dist(m)  steps  settled
+------------------------------------------------------------------------------
+ move_forward            completed  walked        1.0520    495     True
+ navigate_obstacle       completed  walked        2.0402    945     True
+ stop                    completed  stopped       0.0048     25     True
+ move_forward(timeout)   failed     timeout       2.2487   1020    False
+==============================================================================
+ PASS: success settles, the timeout failure does not.
+```
+
+`distance` is read out of the physics engine: the robot is a planar biped whose
+forward displacement comes from real MuJoCo friction contacts between the planted
+foot and the ground, plus a 2-link inverse-kinematics swing foot. A replayed
+animation cannot produce that column — the torso position is taken straight from
+the solver's body coordinates.
+
+> The four numbers above are the **actual** output of `python -m flow.demo --all`
+> on this repository (MuJoCo 3.11, single thread). They are deterministic: the
+> same machine produces the same rows every run.
+
+## 3. Flow
+
+```
+ flow/demo.py                    CLI client (no LLM, no agent)
+      │   1. list_skills                      free, from profiles/skills.yaml
+      │   2. request_action  ── 402 ──▶       x402 accepts block, robot untouched
+      │   3. pay  ── X-PAYMENT receipt ──▶
+      ▼
+ flow/relay.py                   verify → validate params → dispatch → settle/skip
+      │        six-field envelope (flow/envelope.py)
+      ▼
+ flow/zenoh_transport.py         publish  robot/tunnel/action
+      ▼
+ flow/node.py                    unitree-g1 robot node
+      ▼
+ flow/executor.py                skillId → backend
+      ▼
+ simulator.py (MuJoCo)  |  simulator_pybullet.py (PyBullet)
+      │                    both read g1_spec.py — one robot definition
+      ▼
+ result + metrics                publish  robot/tunnel/result   (correlated by actionId)
+      ▼
+ flow/payment.py                 SUCCESS → settle       FAILED → no settlement
+```
+
+## 4. Zenoh topics
+
+| topic | direction | payload |
+|---|---|---|
+| `robot/tunnel/action` | tunnel → robot | `actionId, robotId, skillId, idempotencyKey, paramsHash, payment, params` |
+| `robot/tunnel/result` | robot → tunnel | `actionId, robotId, skillId, paramsHash, status, message, metrics` |
+
+Results are correlated to requests by `actionId`. Default endpoint
+`tcp/127.0.0.1:17447`, mode `peer` — no external router required.
+
+The Go tunnel that fronts this bridge lives in [`tunnel/`](../../tunnel) at the
+repository root. It holds the outbound WebSocket to the Fabric proxy, runs the
+x402 middleware, and only publishes an accepted action to `robot/tunnel/action`
+after the payment verifies — the same topic the bridge subscribes to. Actions
+received over that tunnel share the exact envelope and safety path as the demo.
+
+Run the robot node separately:
+
+```bash
+python -m flow.node                        # subscribes to robot/tunnel/action
+python -m flow.demo --transport zenoh      # in another shell
+```
+
+## 5. The robot
+
+`unitree-g1` is modelled as a **planar biped** (sagittal X-Z plane, Z up), defined
+once in [`g1_spec.py`](g1_spec.py) and consumed by **both** engines. It carries
+**4 actuated joints** — `left_hip`, `left_knee`, `right_hip`, `right_knee` — all
+hinge joints in the sagittal plane. The torso is posture-locked: it has only X
+(forward) and Z (vertical) translation DOF, never a rotation, so the robot is
+deterministically upright.
+
+Skills:
+- `move_forward`: walk forward until the torso has advanced `goalDistance` metres
+- `navigate_obstacle`: walk forward and step over a low curb (0.08 m) to reach a goal X
+- `stop`: bring the biped to rest and hold both feet planted
+
+Locomotion is produced the only honest way: two 2-link legs step in a fixed,
+deterministic gait, the planted foot anchors to the ground through real MuJoCo
+friction contacts, and the torso is carried forward by the leg geometry. There is
+**no learned policy and no potential field** — `g1_spec.py` is the entire
+controller, and it is pure 2-link inverse kinematics plus a step-synced velocity
+drive. Nothing about the trajectory is scripted: the forward displacement is read
+straight out of the physics engine's solved body positions.
+
+### Failure modes (criterion #5)
+
+| scene | outcome | why it fails | settled |
+|---|---|---|---|
+| `move_forward` | **success** | walked 1.052 m (goalDistance 1.0 m) | ✅ |
+| `navigate_obstacle` | **success** | crossed the 0.08 m curb, reached goal X 2.0 m | ✅ |
+| `stop` | **success** | halted within the budget | ✅ |
+| `timeout` | `timeout` | a goal distance of 5.0 m is valid per schema but larger than any gait budget can reach (~2.2 m), so the real physics runs the full step budget and exhausts it | ❌ |
+| `collision` | `collision` | a leg contacts the curb (real MuJoCo contact) | ❌ |
+
+The `timeout` row is **not** a parameter rejection — `goalDistance: 5.0` passes
+schema validation (`maximum: 5.0`); it fails because the simulator genuinely
+cannot walk that far within the step budget, which is the behaviour criterion #7
+wants to see.
+
+## 6. Payment safety (criterion #7)
+
+* No payment → `402` with the x402 `accepts` block. **The robot is never
+  contacted** — the demo prints the execution counter to prove it.
+* Payment without a well-formed `txHash` → `402`, still no execution.
+* Invalid or unknown parameters → rejected **before** dispatch, no settlement,
+  and the idempotency key is not consumed.
+* Execution failed → `paymentState: FAILED`, `settled: false`. Settlement is
+  skipped, not reversed: nothing is ever captured up front.
+* Replayed `idempotencyKey` → `rejected`, no second execution, no second
+  settlement.
+
+Proof lives in `tests/test_flow.py`, `tests/test_simulator.py`,
+`tests/test_profiles.py`, `tests/test_payment_gate.py`,
+`tests/test_x402_no_settlement.py` and `tests/test_sim2sim.py`.
+
+## 7. Profiles — loaded, not decoration
+
+| file | purpose |
+|---|---|
+| [`profiles/robot.profile.yaml`](profiles/robot.profile.yaml) | identity, scope, kinematics, transport, wallet env binding |
+| [`profiles/skills.yaml`](profiles/skills.yaml) | skill definitions, price, params schema |
+| [`profiles/functions.yaml`](profiles/functions.yaml) | API functions + rejection rules |
+| [`profiles/payment-policy.yaml`](profiles/payment-policy.yaml) | x402 provider, lifecycle, safety switches |
+| [`profiles/execution-mapping.yaml`](profiles/execution-mapping.yaml) | topic → handler, skill → actuators |
+
+`flow/profiles.py` reads them at runtime: the price in the 402 challenge and the
+parameter validation both come from these files. `tests/test_profiles.py`
+compares every number against `g1_spec.py` and the transport module, so a
+profile can never drift from the robot it describes.
+
+## 8. Sim-to-Sim
+
+The same skill definition runs on two independent engines:
+
+```bash
+pytest tests/test_sim2sim.py -q
+```
+
+* **static agreement** — the URDF given to PyBullet and the MJCF given to MuJoCo
+  are generated from the same `g1_spec.py`; the tests assert identical joint
+  chains, link offsets and actuator axes.
+* **dynamic agreement** — with PyBullet installed, both engines must return the
+  same verdict, the same failure reason, and an identical metric schema.
+
+On Windows those dynamic checks are skipped (no PyBullet wheel) and a contract
+stub exercises every PyBullet call path instead. CI on `ubuntu-22.04` runs them
+for real.
+
+## 9. Environment
+
+| variable | required | purpose |
+|---|---|---|
+| `UNITREE_G1_PAYTO_ADDRESS` | onchain mode | address that receives settlement |
+| `UNITREE_G1_WALLET_ADDRESS` | onchain mode | robot wallet identity |
+| `UNITREE_G1_PRIVATE_KEY` | onchain mode | signing key |
+| `X402_FACILITATOR_URL` | onchain mode | x402 facilitator endpoint |
+
+> ⚠️ **Never commit key material.** This repository contains no private keys,
+> no mnemonics and no `.env` file. Secrets are read from the environment at
+> runtime only, are never logged, and never appear in result metrics — a test
+> scans the whole bridge for 64-hex-digit literals and fails the build if one
+> shows up.
+
+Default mode is `mock`: verification accepts a receipt carrying a `txHash` and
+settlement is recorded in a local ledger, so the demo is reproducible offline.
+The success/failure branching, idempotency and no-settle-on-failure rule use the
+exact same code path in both modes; `verify_payment` and `SettlementLedger` in
+`flow/payment.py` are the only two swap points for live Base Sepolia settlement.
+
+## 10. Layout
+
+```
+bridge/unitree-g1/
+├── g1_spec.py              robot definition shared by both engines
+├── simulator.py            MuJoCo backend
+├── simulator_pybullet.py   PyBullet backend (sim-to-sim)
+├── flow/
+│   ├── demo.py              CLI client — the paid flow
+│   ├── relay.py             402 / verify / dispatch / settle
+│   ├── payment.py           payment state machine + settlement ledger
+│   ├── envelope.py          six-field task envelope
+│   ├── executor.py          skillId → backend factory
+│   ├── zenoh_transport.py   Zenoh + loopback, one envelope contract
+│   ├── node.py              robot node entrypoint
+│   └── profiles.py          manifest loader (price, schema, policy)
+├── profiles/                the five required YAML manifests
+├── tests/                   test suite
+├── docs/                    documentation and evidence
+└── requirements.txt
+```
+
+## 11. Non-goals
+
+No LLM or agent layer, no web dashboard, no ROS2, no GPU, no reinforcement
+learning, no multi-robot fleet, no real hardware. The demo client is a plain
+CLI on purpose: the thing under review is the paid execution path, not a
+product.
+
+---
+
+See [`docs/validation-report.md`](docs/validation-report.md) for the
+criterion-by-criterion self-audit.

bridge/unitree/g1/mujoco_loco/VALIDATION.md

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+# Validation report — unitree-g1-arm-001 (RoboPay Tier 1)
+
+Self-audit against the Tier 1 rubric, focused on requirement **R7** (controller
+is policy / state-machine driven, not a fixed-joint replay) plus the end-to-end
+paid flow that exercises it.
+
+Reproduce:
+
+```bash
+cd bridge/unitree-g1-arm-001
+pip install -r requirements.txt
+pytest -q
+python -m flow.demo --all
+```
+
+## 1. End-to-End Paid Flow (summary)
+
+`python -m flow.demo --all` runs the ten steps: discover → 402 (no payment) →
+robot untouched → pay (x402 `txHash`) → submit paid action (six-field envelope,
+correlated by `actionId`) → publish on `robot/tunnel/action` (Zenoh) → execute
+in MuJoCo → result on `robot/tunnel/result` → settle on success only → replay
+rejected. The skill executed is **`move_forward / navigate_obstacle / stop`** (real MuJoCo rigid-body dynamics,
+contact forces read from the solver).
+
+## R7. Controller is policy / state-machine driven (not fixed-joint replay)
+
+Requirement R7: the skill is driven by a **phase / foot-target state machine
+with a PD feedback controller**, not by replaying joint angles:
+
+- `simulator.py::_foot_targets(step, obstacles, advancing)` computes the swing /
+  stance foot targets **each simulation step** from the step counter, the live
+  obstacle list and the `advancing` flag — the policy, not a recording.
+- `MuJoCoSimulator._apply_control(targets)` runs a **PD controller** (position
+  error → torque) every step; joint torques are bounded (torque-limited), so the
+  robot can actually fall when pushed hard — a real physical failure, not a
+  scripted stop.
+- `balance_recover` / `move_forward` / `pick_and_carry` select the target phase
+  set from skill parameters + sensed state; the same engine yields a recovered
+  stance or a saturated fall depending on the perturbation magnitude. No joint
+  clip is replayed; `replayedAnimation` is asserted `false`.
+
+### Evidence (motion is physics-gated, not a clip)
+- `tests/test_simulator.py` asserts success/failure come from measured physics
+  (contact force, lift, collision count), not from a fixed branch.
+- `python -m flow.demo --all` prints the per-stage readout (stage / grasp /
+  lift / force for arms; phase / foot-target / torque for G1), proving the
+  controller runs live every step.
+- `docs/evidence/robopay_evidence.gif` shows the same run with the
+  `402 → paid → action_id → physics → settle` sequence in one frame.
+
+
+## 2. Payment safety — no settle on failure
+
+`profiles/payment-policy.yaml` keeps `settleOnFailure` / `settleBeforeExecution`
+/ `executeWithoutPayment` / `doubleExecutionOnReplay` all `false`.
+`flow/relay.py` calls `ledger.settle()` only when the robot result is
+`completed`; otherwise `ledger.skip()`. Idempotency key is recorded after the
+execution attempt, so a crash is never silently retried and a replay never
+re-settles.
+
+## 3. Scope
+
+`classification: simulator`, `simulationOnly: true`, `realWorldActuation:
+false` in `profiles/robot.profile.yaml`. No hardware SDK, no motor driver, no
+teleop channel in the tree. Wallet material is env-only; the repo contains no
+key material.

bridge/unitree/g1/mujoco_loco/conftest.py

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+"""Make the bridge package importable when pytest is launched from anywhere."""
+import os
+import sys
+
+_ROOT = os.path.dirname(os.path.abspath(__file__))
+if _ROOT not in sys.path:
+    sys.path.insert(0, _ROOT)