README.md

metadata

license: apache-2.0
pretty_name: pilot tasks
tags:
  - robotics
  - task-cards
  - world-model
  - model-predictive-control
configs: []

pilot tasks

Robot tasks written as cards, improved in the open by people and their agents. Each improvement is scored and traceable, and every pilot reads the same cards: cablepilot (cable robots) today, armpilot and dronepilot next.

A card says what a job is, in a vocabulary every body shares. Each body says how, with its own world model and planner. A tuning file says how much for one body. A result says how it went. So a new task starts from the best card so far, not from scratch, and a subtask written for one robot is there for the next.

tasks/<task>/
  card.yaml               body-free: objects, stages (each a skill), fallbacks, done test, lineage
  tuning/<body>.yaml      that body's numbers (cost weights, thresholds), with lineage
  results/<id>.json       one scored run (format pilot-result/1): score, compute, world, seed, card + tuning hashes
  media/<id>.mp4          its film (the plan in purple)
board.json                best result per task x body x world, rebuilt from results/ (scripts/board.py)

Tasks

task	bodies	best so far (sim)	note
clean_the_room	cable	29/36 toys boxed, bedroom, 3 episodes	requested by the Stringman community; sorting is the next card
avoid_furniture	(card only)	open	requested by the Stringman community: place to place, touching nothing. cablepilot check lists what the cable body is missing (a near skill, a furnished room): the first open job
pen_in_cup	(card only)	no hub result yet	armpilot's card, unchanged; the cable robot runs it but cannot watch aligned(pen, up) yet. Its arm tuning comes with armpilot's release

A card

task: clean_the_room
lineage: {parent: "worldpilot:cards/home/clean_the_room.yaml", by: Ethgar, why: "runnable on today's skills"}
objects:
  stray:   {kind: any, role: held, shape: any, movable: true, each: out_of_place}
  toy_box: {kind: box, role: site, shape: open_container, movable: false}
stages:
  - {name: observe,   goals: [{see: [head_camera, stray]}]}
  - {name: pre_grasp, goals: [{pre_grasp: [hand, stray]}], avoid: [{avoid: [hand, toy_box]}]}
  - {name: grasp,     goals: [{grasp: [hand, stray]}]}
  - {name: over_box,  goals: [{pre_inside: [stray, toy_box.interior]}], invariants: [{grasp: [hand, stray]}]}
  - {name: release,   goals: [{release: [hand, stray]}]}
fallbacks:
  - {when: {broken: {grasp: [hand, stray]}}, go: pre_grasp}
done: {inside: [stray, toy_box.interior]}

No metres, colours or positions: the body's eyes bind stray and toy_box when it runs, and the body's tuning holds the numbers. Predicates and skills: cablepilot/core/cards.py (PREDICATES, SKILLS).

Contributing (people and agents)

1. cablepilot fetch <task>, then cablepilot run tasks/<task>/card.yaml --world bedroom to reproduce the best result.
2. Change one thing: a tuning number, a stage, a fallback, or write a new card from an old one with lineage set.
3. Score it on the same worlds and seeds, then cablepilot submit runs/<run> --task <task> .... That opens a pull request here, and its page is the discussion thread.
4. The maintainers re-run the PR in simulation and post the result as a comment. Merged results update board.json.

Good first contributions: a missing skill that cablepilot check reports on an existing card (window washing needs wipe; folding needs support_both), a second seed or room for an existing result, or the first real-robot log.

Every card must stay body-free, every result must come with its result.json, and a measured regression is welcome too.