SKILL.md

metadata

name: rtdetr-coco-r18
description: >-
  S1 object detector. Capabilities: detect on person, cup, bottle, bowl, chair,
  table. RT-DETR-L (Real-Time DEtection TRansformer, large variant) trained on
  COCO and exported to ONNX. Runs on the camera tee and publishes
  ObjectsMetadata to /openral/perception/objects. 80 COCO categories. Apache-2.0
  weights. Reference latency ~20 ms on GPU, ~45 ms on CPU. This is the detector
  rSkill kind contract. 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-rtdetr_coco_r18-any-coco-fp32
  manifest: ./rskill.yaml
  role: s1
  kind: detector
  embodiment_tags:
    - any
  actions:
    - detect
  objects:
    - person
    - cup
    - bottle
    - bowl
    - chair
    - table
  scenes:
    - tabletop
    - kitchen
    - indoor
  sensors_required:
    - rgb
  runtime: onnx
  quantization: fp32/onnx
  chunk_size: 1
  latency_budget:
    per_chunk_ms: 50
  license_code: Apache-2.0
  license_weights: apache-2.0
  weights_uri: local://rskills/rtdetr-coco-r18
  source_repo: hf://PekingU/rtdetr_r18vd_coco_o365
  paper_url: https://arxiv.org/abs/2304.08069

rtdetr-coco-r18 — 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 object detector (role: s1, kind: detector). RT-DETR-L (Real-Time DEtection TRansformer, large variant) trained on COCO and exported to ONNX. Runs on the camera tee and publishes ObjectsMetadata to /openral/perception/objects. 80 COCO categories. Apache-2.0 weights. Reference latency ~20 ms on GPU, ~45 ms on CPU. This is the detector rSkill kind contract.

Capabilities

• Verbs: detect
• Objects: person · cup · bottle · bowl · chair · table
• Scenes: tabletop · kitchen · indoor
• 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-rtdetr_coco_r18-any-coco-fp32")
# 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.