core/demos/amr_decision_substrate/README.md

AMR Decision Substrate Demo

This demo is robotics-adjacent, not a robotics stack.

It uses simulated abstract situation records to show CORE as a decision and accountability substrate around a bounded AMR-style proceed / stop / refuse choice. The inputs are not camera, LiDAR, odometry, SLAM, localization, motor, or fleet-control data.

Claims-ledger framing: this is a preparation artifact over simulated records. It is not deployment readiness, not perception, not motion planning, and not motor control. The demo proves only its local trace/refusal/replay surface over these fixtures. It does not imply a CORE expert domain, a robotics capability claim, or working vision/motor. Per the ledger, text is the active capability; audio is substrate with the gate CLOSED; vision and motor are proposed only.

What is real CORE here:

• ChatRuntime
• CognitiveTurnPipeline.run(...)
• recognition-side typed refusal propagation
• CognitiveTurnResult.trace_hash
• CORE Trace Protocol canonical JSONL events
• verify_chain(...) replay validation

What is simulated:

• the AMR situation record
• the tiny policy reducer that maps already-abstracted facts to PROCEED, STOP, or REFUSE

The demo refuses under-determined input instead of guessing. It also runs the same scenarios twice through fresh runtime instances and asserts byte-identical trace JSONL.

Run from the repository root:

UV_PROJECT_ENVIRONMENT=/tmp/core-amr-decision-uv uv run python demos/amr_decision_substrate/run_demo.py

Artifacts are written to:

demos/amr_decision_substrate/out/

The important artifact is summary.json; trace_a.jsonl and trace_b.jsonl are the two replay runs that must match byte-for-byte.