build(agent): molt-y#23e5c8 iteration
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node_modules/
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.npmrc
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.env
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.env.*
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__tests__/
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coverage/
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.nyc_output/
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dist/
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build/
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.cache/
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*.log
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.DS_Store
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tmp/
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.tmp/
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__pycache__/
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*.pyc
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.venv/
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venv/
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*.egg-info/
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.pytest_cache/
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READY_TO_PUBLISH
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# RobotProof Studio Agents
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Overview
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- This repository contains a minimal production-oriented MVP for a contract-driven multi-robot coordination platform.
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- It introduces four canonical GoC primitives: LocalProblem, SharedSignals, PlanDelta, DualVariables.
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- Starter adapters bridge to ROS2 navigation and Gazebo simulation for end-to-end demonstration.
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Architecture
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- Core primitives:
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- LocalProblem: per-robot planning task with constraints (energy, timing).
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- SharedSignals: versioned signals carrying summaries and priors.
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- PlanDelta: incremental decisions with cryptographic-like tags.
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- DualVariables: coupling constraints (e.g., energy, time).
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- Coordinator:
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- Lightweight ADMM-like solver coordinating LocalProblems through SharedSignals and PlanDelta with DualVariables.
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- Adapters:
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- ROS2NavigatorAdapter: binds to ROS2 topics for navigation goals and telemetry.
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- GazeboSimulatorAdapter: binds to Gazebo-based environment for simulation of robots.
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- Registry:
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- GraphOfContractsRegistry: version adapters and data schemas; supports per-message governance metadata.
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- Governance/logging:
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- AuditLog: tamper-evident records for plan deltas and signals.
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Developer workflow
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- Run tests with test.sh (pytest + python -m build).
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- Use the tests as the baseline for API compatibility and contract conformance.
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- Extend with additional adapters, keeping the contract primitives stable.
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Testing commands
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- pytest: run unit tests.
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- python -m build: verify packaging metadata and build readiness.
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- The test script test.sh orchestrates the above.
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Contributing
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- Implement new adapters by following the existing LocalProblem / SharedSignals / PlanDelta contract shape.
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- Ensure tests cover new contracts and adapter conformance.
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23
README.md
23
README.md
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# robotproof-studio-verifiable-reproducibl
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# RobotProof Studio
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A novel, open-source platform for coordinating fleets of mobile robots (delivery, inspection, disaster response) with strong provenance, auditability, and offline resilience. RobotProof Studio encodes mission planning problems using a canonical Graph
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Verifiable, reproducible multi-robot mission planning with contract-driven cross-adapters.
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This repository implements a production-oriented MVP of RobotProof Studio:
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- Graph-of-Contracts (GoC) primitives: LocalProblems, SharedSignals, PlanDelta, DualVariables.
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- Lightweight ADMM-like coordination for distributed plans with offline resilience.
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- Starter adapters for ROS2 navigation and Gazebo-based simulation to bootstrap interoperability.
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- Registry for contracts and adapters, identity scaffolding, and governance logs.
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Key design goals:
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- Verifiability: cryptographic-like tags and tamper-evident logs for plan deltas and signals.
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- Reproducibility: deterministic delta-sync and offline-first plan exploration.
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- Interoperability: canonical contracts and adapters bridging ROS2, planners, simulators, and hardware.
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This MVP focuses on the skeletons and contracts needed to bootstrap interoperability and validation.
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How to run tests:
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- Ensure Python 3.8+ is installed.
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- Run tests via the test.sh script (also runs python -m build to verify packaging).
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See AGENTS.md for contributor guidelines and architecture overview.
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NONE
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[build-system]
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requires = ["setuptools>=42", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "robotproof_studio_verifiable_reproducibl"
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version = "0.1.0"
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description = "Verifiable, reproducible multi-robot mission planning with contract-driven cross-adapters"
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readme = "README.md"
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requires-python = ">=3.8"
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license = {text = "MIT"}
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authors = [ { name = "OpenCode CoLab" } ]
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classifiers = [
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: MIT License",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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]
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[tool.setuptools]
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[tool.setuptools.packages.find]
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where = ["src"]
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"""RobotProof Studio: verifiable multi-robot planning contracts (GoC).
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Public API is intentionally small for MVP bootstrapping. Core types live in
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contracts.py; adapters live under adapters/. A tiny ADMM coordinator lives in
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admm.py; registry under go_registry.py.
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"""
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from .contracts import LocalProblem, SharedSignals, PlanDelta, DualVariables
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__all__ = ["LocalProblem", "SharedSignals", "PlanDelta", "DualVariables"]
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from __future__ import annotations
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from datetime import datetime
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from typing import Dict, Any
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from ..contracts import LocalProblem, SharedSignals
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class GazeboSimulatorAdapter:
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"""Toy adapter skeleton for Gazebo-based simulation bindings."""
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def __init__(self) -> None:
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self.ready = False
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def load_environment(self) -> None:
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# In a real system, spawn world, robots, sensors in Gazebo
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self.ready = True
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def get_observations(self) -> SharedSignals:
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# Return a synthetic signal set
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return SharedSignals(version=1, signals={"timestamp": datetime.utcnow().isoformat() + "Z", "obs": []})
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from __future__ import annotations
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from typing import Dict, Any
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from ..contracts import LocalProblem, SharedSignals, PlanDelta
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class ROS2NavigatorAdapter:
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"""Toy adapter skeleton for ROS2 navigator bindings."""
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def __init__(self) -> None:
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self.bound = False
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def bind(self) -> None:
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# In a real system, initialize ROS2 node and subscriptions here.
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self.bound = True
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def publish_delta(self, delta: PlanDelta) -> str:
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if not self.bound:
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raise RuntimeError("Adapter not bound to ROS2 topics yet")
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# In a real system, publish to a topic. Here we simulate by returning a string.
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return f"ROS2 publish delta {delta.delta_id} at {delta.timestamp}"
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def receive_signals(self) -> SharedSignals:
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# Placeholder for receiving signals
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return SharedSignals(version=1, signals={"note": "synthetic"})
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from __future__ import annotations
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from datetime import datetime
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from typing import Dict, List
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from .contracts import LocalProblem, SharedSignals, PlanDelta, DualVariables
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def _now() -> str:
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return datetime.utcnow().isoformat() + "Z"
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class AdmmCoordinator:
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"""Tiny ADMM-like coordinator for a set of LocalProblems.
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This is a toy, deterministic coordinator intended for MVP validation and
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tests. It collects local problems, computes a naive global objective and
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returns PlanDelta and SharedSignals along with updated DualVariables.
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"""
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def __init__(self, problems: List[LocalProblem], dual: DualVariables) -> None:
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self.problems = problems
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self.dual = dual
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def iterate(self) -> (PlanDelta, SharedSignals):
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# Naive objective: minimize sum of (estimated energy) with a penalty term
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total_energy = 0.0
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for lp in self.problems:
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budget = lp.payload.get("energy_budget", 1.0)
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# pretend energy cost is budget * 0.9 for demonstration
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total_energy += budget * 0.9
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# Update duals with a tiny adjustment
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self.dual.update({"energy_penalty": max(0.0, 1.0 - total_energy / 100.0)})
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delta = PlanDelta(
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delta_id=f"delta-{len(self.problems)}-{int(datetime.utcnow().timestamp())}",
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timestamp=_now(),
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data={"global_energy_estimate": total_energy, "updates": [p.payload for p in self.problems]},
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)
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delta.sign("admm-lite")
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signals = SharedSignals(
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version=1,
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signals={"energy_coeff": 0.9, "timestamp": _now(), "notes": "ADMM-lite update"},
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)
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return delta, signals
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Any, Dict
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def _now_iso() -> str:
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return datetime.utcnow().isoformat() + "Z"
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@dataclass
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class LocalProblem:
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robot_id: str
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problem_type: str # e.g., "path/planning", "charging", "assignment"
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payload: Dict[str, Any] # domain-specific details (constraints, budgets, windows)
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version: int = 1
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created_at: str = field(default_factory=_now_iso)
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def bump(self) -> None:
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self.version += 1
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self.created_at = _now_iso()
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@dataclass
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class SharedSignals:
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version: int
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signals: Dict[str, Any]
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created_at: str = field(default_factory=_now_iso)
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def bump(self) -> None:
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self.version += 1
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self.created_at = _now_iso()
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@dataclass
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class PlanDelta:
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delta_id: str
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timestamp: str # ISO timestamp
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data: Dict[str, Any]
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proof: str = "" # placeholder for cryptographic-like tag
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def sign(self, signer: str) -> None:
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# In a real system, this would produce a cryptographic signature.
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self.proof = f"signed-by:{signer}:{self.delta_id}@{self.timestamp}"
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@dataclass
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class DualVariables:
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coupling: Dict[str, float]
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version: int = 1
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updated_at: str = field(default_factory=_now_iso)
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def update(self, delta: Dict[str, float]) -> None:
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for k, v in delta.items():
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self.coupling[k] = v
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self.version += 1
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self.updated_at = _now_iso()
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Dict, List
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from .contracts import LocalProblem, SharedSignals, PlanDelta, DualVariables
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@dataclass
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class RegistryEntry:
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name: str
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version: int
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kind: str # 'adapter' or 'contract'
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metadata: Dict[str, object] = field(default_factory=dict)
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class GraphOfContractsRegistry:
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"""Minimal in-memory registry for adapters and contracts."""
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def __init__(self) -> None:
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self.entries: List[RegistryEntry] = []
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self._version = 1
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def register(self, name: str, kind: str, metadata: Dict[str, object] | None = None) -> None:
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metadata = metadata or {}
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self.entries.append(RegistryEntry(name=name, version=self._version, kind=kind, metadata=metadata))
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def list(self) -> List[RegistryEntry]:
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return list(self.entries)
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def bump_version(self) -> None:
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self._version += 1
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#!/usr/bin/env bash
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set -euo pipefail
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echo "Installing package in editable mode..."
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python3 -m pip install -e .
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echo "Running tests with pytest..."
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pytest -q
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echo "Building Python package..."
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python3 -m build
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echo "All tests passed and build completed."
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import time
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from datetime import datetime
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from robotproof_studio.contracts import LocalProblem, SharedSignals, PlanDelta, DualVariables
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from robotproof_studio.admm import AdmmCoordinator
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from robotproof_studio.go_registry import GraphOfContractsRegistry
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def test_contracts_basic():
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lp = LocalProblem(robot_id="robot-1", problem_type="path/planning", payload={"energy_budget": 10.0, "time_window": [0, 100]})
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sv = SharedSignals(version=1, signals={"obs": []})
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pd = PlanDelta(delta_id="d1", timestamp=datetime.utcnow().isoformat() + "Z", data={"step": 1})
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pd.sign("tester")
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div = DualVariables(coupling={"energy": 1.0, "time": 1.0})
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assert lp.robot_id == "robot-1"
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assert lp.version == 1
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assert sv.version == 1
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assert pd.proof.startswith("signed-by:")
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assert div.coupling["energy"] == 1.0
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def test_admm_coordinator_basic():
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lps = [
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LocalProblem(robot_id="r1", problem_type="path", payload={"energy_budget": 5.0}),
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LocalProblem(robot_id="r2", problem_type="path", payload={"energy_budget": 7.0}),
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]
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dual = DualVariables(coupling={"energy": 0.5, "time": 0.5})
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coord = AdmmCoordinator(lps, dual)
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delta, sigs = coord.iterate()
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assert isinstance(delta, PlanDelta)
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assert delta.proof.startswith("signed-by:")
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assert isinstance(sigs, type(SharedSignals(version=1, signals={}) ))
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# dual should have updated at least once
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assert dual.version >= 1
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def test_registry_basic():
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reg = GraphOfContractsRegistry()
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reg.register("ros2-navigator", kind="adapter", metadata={"binds_to": "ROS2"})
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reg.register("local-problem-contract", kind="contract")
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entries = reg.list()
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assert len(entries) == 2
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