SKILL.md

metadata

name: robometer-4b
description: >-
  S2 task-progress / reward monitor. Capabilities: monitor on task progress,
  task success. Robometer-4B (Qwen3-VL-4B robotic reward foundation model, arXiv
  2603.02115) as an NF4 reward rSkill. Runs parallel to a VLA: given rollout
  frames + the task instruction it emits per-frame normalized progress (0-1) and
  success probability, queried on demand by the Reasoner. Advisory-only — never
  gates motors. Embodiment-agnostic. Apache-2.0. ADR-0057. Discovery view of an
  OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via
  rSkill.from_pretrained + the robot HAL.
metadata:
  openral_rskill: true
  schema_version: 0.1
  rskill_id: OpenRAL/rskill-robometer-4b-nf4
  manifest: ./rskill.yaml
  role: s2
  kind: reward
  embodiment_tags:
    - any
  actions:
    - monitor
  objects:
    - task progress
    - task success
  scenes:
    - tabletop
    - kitchen
    - indoor
    - manipulation
  sensors_required:
    - rgb
  runtime: pytorch
  quantization: int4/pytorch
  min_vram_gb:
    fp32: 18
    bf16: 9
    int4: 3.6
  chunk_size: 1
  latency_budget:
    per_chunk_ms: 3000
  license_code: Apache-2.0
  license_weights: apache-2.0
  weights_uri: hf://OpenRAL/rskill-robometer-4b-nf4
  source_repo: hf://robometer/Robometer-4B@beef63bc914c5c189329d49c6d712d96d632aa34

robometer-4b — rSkill discovery view

Generated view, not a hand-written skill. This SKILL.md is a discovery-only mirror of rskill.yaml, produced by tools/generate_rskill_skillmd.py. It lets tools that read the standard agent-skill format find and reason about this OpenRAL rSkill. The rskill.yaml manifest is the single source of truth (CLAUDE.md §1.3). Do not edit by hand — edit the manifest and regenerate.

What it is

An OpenRAL task-progress / reward monitor (role: s2, kind: reward). Robometer-4B (Qwen3-VL-4B robotic reward foundation model, arXiv 2603.02115) as an NF4 reward rSkill. Runs parallel to a VLA: given rollout frames + the task instruction it emits per-frame normalized progress (0-1) and success probability, queried on demand by the Reasoner. Advisory-only — never gates motors. Embodiment-agnostic. Apache-2.0. ADR-0057.

Capabilities

• Verbs: monitor
• Objects: task progress · task success
• Scenes: tabletop · kitchen · indoor · manipulation
• Embodiments: any

Why this is discovery-only

An agent skill is natural-language instructions loaded into an LLM's context. An rSkill is an executable artifact: it carries a typed capability/embodiment contract, model weights, a runtime, and a license/provenance gate — none of which fit in freeform markdown. So an agent can use this view to select the right skill, but cannot execute it by loading this file. Execution always goes through the OpenRAL loader and the robot HAL.

License

• Code: Apache-2.0.
• Weights: apache-2.0 — permissive / commercial-use OK

How to actually run it (not via an agent harness)

from openral_rskill import rSkill

skill = rSkill.from_pretrained("OpenRAL/rskill-robometer-4b-nf4")
# the loader validates embodiment / sensors / runtime / quantization against the target
# RobotDescription and enforces the weight-license gate before any weights load.

See rskill.yaml for the authoritative, validated manifest.