SKILL.md

metadata

name: xr1-robocasa365
description: >-
  S1 Vision-Language-Action policy. Capabilities: generalist, pick, place, open,
  close on kitchen_object. Apache-2.0 XR-1 RoboCasa365 checkpoint. The adapter
  keeps seven frames, samples four at interval two, converts OpenRAL's 16-D
  quaternion layout to XR-1's 14-D axis-angle state, and replays sixteen decoded
  actions per query. 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-xr1-panda_mobile-robocasa365-nf4
  manifest: ./rskill.yaml
  role: s1
  kind: vla
  model_family: xr1
  embodiment_tags:
    - panda_mobile
  actions:
    - generalist
    - pick
    - place
    - open
    - close
  objects:
    - kitchen_object
  scenes:
    - kitchen
  sensors_required:
    - rgb:observation.images.camera1
    - rgb:observation.images.camera2
    - rgb:observation.images.camera3
  state_dim: 16
  action_dim: 12
  runtime: pytorch
  quantization: int4/pytorch
  chunk_size: 16
  n_action_steps: 16
  latency_budget:
    per_chunk_ms: 120000
  license_code: Apache-2.0
  license_weights: apache-2.0
  weights_uri: hf://OpenRAL/rskill-xr1-panda_mobile-robocasa365-nf4
  source_repo: >-
    hf://XiaomiRobotics/Xiaomi-Robotics-1-RoboCasa365@0d1aa76d0d82debc9b611e4d1e231096434d5be4
  paper_url: https://arxiv.org/abs/2607.15330

xr1-robocasa365 — 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 Vision-Language-Action policy (role: s1, kind: vla). Apache-2.0 XR-1 RoboCasa365 checkpoint. The adapter keeps seven frames, samples four at interval two, converts OpenRAL's 16-D quaternion layout to XR-1's 14-D axis-angle state, and replays sixteen decoded actions per query.

Capabilities

• Verbs: generalist · pick · place · open · close
• Objects: kitchen_object
• Scenes: kitchen
• Embodiments: panda_mobile

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-xr1-panda_mobile-robocasa365-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.