rskill-playbook-stage_for_manipulation

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 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.

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

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 — the decision procedure itself.