24 lines
1.4 KiB
Markdown
24 lines
1.4 KiB
Markdown
# SpaceSafeML: Certification, Benchmark, and Governance Framework for Onboard AI in Space Robotics
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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.
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What you can expect in this MVP
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- A Python package named `spacesafeml_certification_benchmark_and_` with core modules:
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- DSL definitions for LocalCapabilities, SafetyPre/SafetyPostConditions, ResourceBudgets, and DataSharingPolicies
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- A simple verification engine that can generate safety certificates for plans
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- A tiny simulation scaffold with placeholder Gazebo/ROS-like interfaces for fleet scenarios (deterministic and replayable)
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- A tamper-evident ledger to audit test results
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- Starter adapters for planning and perception modules
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- A basic test suite to validate core behavior and a test launcher script `test.sh` that runs tests and packaging verification
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- Documentation file `AGENTS.md` describing architecture and contribution rules
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Getting started
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- Install Python 3.8+ and run tests via `bash test.sh`.
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- Explore the MVP modules under `spacesafeml_certification_benchmark_and_`.
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This project intentionally remains minimal yet extensible to accommodate future MVP expansion consistent with the SpaceSafeML vision.
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## License
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MIT
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