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Problem: Space robotics fleets require autonomous planning with strict energy budgets and intermittent connectivity. Conventional CPU-based planners struggle to deliver low-latency, energy-efficient inference for onboard AGI reasoning, while external
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README.md

NeuPlan MVP

This repository contains a minimal, production-oriented scaffold for a neuromorphic planning stack intended for onboard autonomous planning in space robotics contexts. The MVP focuses on providing a DSL, a toy neuromorphic intermediate representation (N-IR), a backend shim, and a deterministic onboard runtime that can be extended to real neuromorphic hardware.

Highlights

  • DSL for LocalProblem, PlanDelta, and SharedVariables
  • Toy translation to N-IR suitable for testing and integration
  • Loihi-like backend shim with deterministic latency/energy model
  • Onboard runtime to execute planning within strict budgets
  • Basic tests ensuring end-to-end flow and a minimal demo CLI

Installation and testing

  • Run: ./test.sh (requires Python 3.9+ and build tools; creates a virtual environment automatically during build)

This is an early-stage MVP; expect to see iterative improvements with governance, safety, and HIL features in subsequent iterations.