docs: HF model card for OpenRAL/rskill-playbook-stage_for_manipulation v0.1.0

README.md

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+---
+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.