Robots Understanding Context ​

This example shows a practical .me pattern for robot reasoning:

• one shared world object
• multiple robot-specific contexts
• pointers from each robot to the same object and its current context
• broadcast derivations that turn context into action policy
• explain() to audit why each robot decided to act

Full Script ​

import ME from "this.me";

const me = new ME();

me["@"]("robot-context-lab");

// Shared object
me.objects.canister7.name("Blue canister");
me.objects.canister7.massKg(6);
me.objects.canister7.fragile(true);
me.objects.canister7.sterile(false);

// Contexts
me.contexts.warehouse.pickupZone(true);
me.contexts.warehouse.sterileZone(false);
me.contexts.warehouse.movingVehicles(false);

me.contexts.hospital.pickupZone(false);
me.contexts.hospital.sterileZone(true);
me.contexts.hospital.movingVehicles(false);

me.contexts.street.pickupZone(false);
me.contexts.street.sterileZone(false);
me.contexts.street.movingVehicles(true);

// Robots
me.robots.loader.name("Loader-1");
me.robots.loader.liftCapacityKg(20);
me.robots.loader.target["->"]("objects.canister7");
me.robots.loader.context["->"]("contexts.warehouse");

me.robots.nurse.name("NurseBot-2");
me.robots.nurse.liftCapacityKg(12);
me.robots.nurse.target["->"]("objects.canister7");
me.robots.nurse.context["->"]("contexts.hospital");

me.robots.courier.name("Courier-3");
me.robots.courier.liftCapacityKg(18);
me.robots.courier.target["->"]("objects.canister7");
me.robots.courier.context["->"]("contexts.street");

// Broadcast context-aware understanding
me.robots["[i]"]["="]("canLift", "target.massKg "]("objects.canister7");
me.robots.nurse.target["->"]("objects.canister7");
me.robots.courier.target["->"]("objects.canister7");

All robots point to the same object. .me does not duplicate the world model just because multiple agents observe it.

2) Context lives in structure ​

me.robots.loader.context["->"]("contexts.warehouse");
me.robots.nurse.context["->"]("contexts.hospital");
me.robots.courier.context["->"]("contexts.street");

The difference is not hidden in imperative branching code. It is explicit in the graph:

• warehouse context makes fragility relevant to grip
• hospital context makes sterility relevant to handling
• street context makes traffic relevant to motion

3) One policy can fan out to every robot ​

me.robots["[i]"]["="]("needsSterileHandling", "context.sterileZone && !target.sterile");

The derivation is declared once and automatically specialized for each robot through its local pointers.

4) Meaning changes when context changes ​

The same canister is:

• cargo for Loader-1
• a sterile-risk object for NurseBot-2
• a yield-sensitive object for Courier-3

If the object becomes sterile or the traffic clears, the interpretation updates without rewriting the robot logic.

5) You can audit the decision ​

me.explain("robots.nurse.canProceed");

This makes the robot's decision inspectable:

• what expression was used
• which dependencies were read
• which context fields affected the outcome

Mental Model ​

.me is useful for robotics because it lets you model:

• shared world state
• subjective robot viewpoints
• context-specific policy
• explainable action flags

with one reactive tree instead of scattered conditionals.

Run ​

node tests/Demos/Robots_Contexts.ts

Where To Go Next ​

• Algebra of Contexts
• Shared Meaning
• Runtime Surface
• Syntax