21 lines
1.3 KiB
Markdown
21 lines
1.3 KiB
Markdown
# EdgeMind: Verifiable Onboard AI Planning Runtime
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EdgeMind provides a modular, contract-based AI planning runtime designed for embedded robotics and space habitats. It supports offline plan generation with safety contracts, a lightweight data-contract layer for cross-vendor interoperability, and an extensible simulation/testbed environment.
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What you get in this repository (production-ready base):
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- Python-based core with a simple DSL-like planning model (Goals, Actions, Plans)
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- Safety contracts and a basic runtime policy engine placeholder
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- Data-contract layer scaffolding (Objects, Morphisms, Functors) with canonical mapping
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- Lightweight planner capable of solving small, constrained planning tasks on edge hardware
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- Tests, packaging metadata, and a small demo CLI
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- Documentation and governance files to guide future contributions
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How to run locally
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- Install dependencies and run tests via test.sh (see root script)
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- Package and build with python3 -m build
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- Run the CLI demo to observe planning behavior
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This repository is designed to be extended in sprint fashion; it starts with a solid core, test coverage, and a path to full production-grade production code.
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Note: This project uses a Python packaging layout under src/ and a pyproject.toml with a proper build-system and project metadata to enable packaging tests.
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