---
language:
- en
license: apache-2.0
pipeline_tag: robotics
tags:
- OpenRAL
- rskill
- any
inference: false
---

# rskill-playbook-stage_for_manipulation

A `kind: playbook` rSkill: a symbolic S2 **decision procedure** the
Reasoner reads, not a neural policy. It carries no weights — the authored
[`PLAYBOOK.md`](./PLAYBOOK.md) *is* its runtime.

## What this skill does

Moves the robot into a manipulation skill's declared pre-grasp / `starting_pose`
and **verifies** it before the manipulation policy runs, reducing grasp failures
caused by a bad initial pose. It reads the target skill's `starting_pose`,
optionally navigates a mobile base so the target sits inside the arm's workspace,
drives the arm to the pre-grasp through the collision-aware MoveGroup approach
skill, confirms the pose with `query_scene`, and only then hands
control back. Concrete walkthrough: the black-bowl example in
[`PLAYBOOK.md`](./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 (`resolve_place`, `execute_rskill`, `query_scene`, `memory_write`). 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 (the pre-grasp verification needs vision). Arm motion /
navigation 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 manipulation skill declares a starting_pose / pre-grasp the robot is not currently in.
- `playbook.done_predicate`: the robot is in the skill's declared pre-grasp / starting pose, verified, and ready to dispatch the manipulation.
- `playbook.max_steps`: 8.

## Quick start

```python
from openral_core.schemas import RSkillManifest

m = RSkillManifest.from_yaml("rskills/stage-for-manipulation/rskill.yaml")
assert m.kind == "playbook" and m.playbook is not None
print(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`](./PLAYBOOK.md) — the decision procedure itself.