rskill-playbook-preflight_reach
A
kind: playbook rSkill: a symbolic S2
decision procedure the
Reasoner reads, not a neural policy. It carries no weights — the authored
PLAYBOOK.md is its runtime.
What this skill does
Before dispatching a manipulation skill, it checks the target is within the
robot's reachable workspace. It reads the reasoner's
## ROBOT self-model
(locomotion, reach, payload), locates the target via spatial memory / detection,
and runs a
reach check. If the target is reachable it allows the
manipulation; if it is out of reach and a mobile base exists it stages an
approach stand pose and re-checks; if the arm is fixed (or the target is over the
payload limit) it does
not dispatch and hands off to a human. Concrete
walkthrough: the mug example in
PLAYBOOK.md.
How it works
This playbook is content, not code. When installed, the reasoner injects
PLAYBOOK.md into its system prompt and follows the SOP, composing tools it
already has (recall_object, resolve_place, query_scene, execute_rskill,
emit_prompt). It is role: s2 and is never dispatched through
ExecuteSkill. Every motion it triggers is an execute_rskill → Action chunk →
C++ safety kernel — the playbook holds no actuation authority (CLAUDE.md §1.1).
Observation → action contract
None. A playbook emits no Action chunks and requires no actuators
(actuators_required: [], chunk_size: 1). Its "output" is the sequence of
tool calls the reasoner makes while following the SOP, bounded by
playbook.max_steps.
How it was authored / Upstream provenance
N/A — a playbook is hand-authored, not trained: it has no weights and no
upstream model. Its provenance is the authoring decision record
(also linked via paper_url). To change behaviour, edit PLAYBOOK.md and bump
version.
Supported robots
Embodiment-agnostic — declares the explicit wildcard embodiment_tags: ["any"]
(never an empty list). Gated by capabilities_required
(has_vision: true — a real RobotCapabilities flag): the loader filters it out
on robots without a camera. Navigation / staging are gated at runtime by the
composed tools, not by this playbook's flags.
Sensors required
None directly. The tools it composes declare their own sensor needs.
Manifest summary
kind: playbook, role: s2, actions: [plan], chunk_size: 1.playbook.trigger: about to dispatch a manipulation skill on a target whose reachability is uncertain.playbook.done_predicate: the target is confirmed within the robot's reachable workspace (or staged so it is), or the task is handed off.playbook.max_steps: 8.
Quick start
1from openral_core.schemas import RSkillManifest
2
3m = RSkillManifest.from_yaml("rskills/preflight-reach/rskill.yaml")
4assert m.kind == "playbook" and m.playbook is not None
5print(m.playbook.trigger)
Reproduction
Packaging-only: the manifest + SOP are validated by
tests/unit/test_playbook_rskill_manifest.py. There is no benchmark number to
reproduce; the playbook's behaviour is exercised by the reasoner integration
tests in later phases.
Evaluation
N/A — no eval/*.json; a playbook produces no benchmarkable policy output.
License
- Code / content: Apache-2.0.
- Weights: none.
See also
PLAYBOOK.md — the decision procedure itself.
