SpaceSafeML: Certification, Benchmark, and Governance Framework for Onboard AI in Space Robotics

This repository provides a minimal, open-source MVP of a modular framework to certify and benchmark onboard AI agents operating in space robotics contexts. It includes a Safety DSL, a verification harness, a lightweight simulation scaffold, a governance ledger, and starter adapters for common onboard stacks.

What you can expect in this MVP

• A Python package named spacesafeml_certification_benchmark_and_ with core modules:
  • DSL definitions for LocalCapabilities, SafetyPre/SafetyPostConditions, ResourceBudgets, and DataSharingPolicies
  • A simple verification engine that can generate safety certificates for plans
  • A tiny simulation scaffold with placeholder Gazebo/ROS-like interfaces for fleet scenarios (deterministic and replayable)
  • A tamper-evident ledger to audit test results
  • Starter adapters for planning and perception modules
• A basic test suite to validate core behavior and a test launcher script test.sh that runs tests and packaging verification
• Documentation file AGENTS.md describing architecture and contribution rules

Getting started

• Install Python 3.8+ and run tests via bash test.sh.
• Explore the MVP modules under spacesafeml_certification_benchmark_and_.

This project intentionally remains minimal yet extensible to accommodate future MVP expansion consistent with the SpaceSafeML vision.

License

MIT