rskill-playbook-verify_outcome
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
Closes the loop after a skill or subtask completes (Inner Monologue). The action
result ("the policy reported done") is not the same as the world state, so this
playbook
verifies what actually happened: it asks the scene a specific yes/no
success question, cross-checks a reward monitor, optionally confirms the object's
pose, and
classifies success or failure with evidence before the reasoner
proceeds. A failure routes to a replan or human handoff — never a silent
"success". Concrete walkthrough: the black-bowl-on-plate 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 (query_scene, query_task_progress, locate_in_view,
memory_write, emit_prompt). It is role: s2 and is never dispatched
through ExecuteSkill. Any motion a downstream replan 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. The scene / reward queries 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: a skill or subtask has just completed and its success is not directly observable from the action result.playbook.done_predicate: the outcome is classified success or failure with evidence, and a failure has triggered a replan or handoff.playbook.max_steps: 6.
Quick start
1from openral_core.schemas import RSkillManifest
2
3m = RSkillManifest.from_yaml("rskills/verify-outcome/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.
