SKILL.md

metadata

name: gr00t-n17-libero
description: >-
  S1 Vision-Language-Action policy. Capabilities: pick, place, open, close on
  bowl, cup, drawer, object. NVIDIA Isaac GR00T N1.7 (3B, Cosmos-Reason2-2B VLM
  backbone) finetuned on the LIBERO benchmark, packaged for OpenRAL. 7-D LIBERO
  action space (delta end-effector 6-DoF + gripper) over two RGB views. Runs
  in-process via lerobot 0.6.0's native GrootPolicy with an NF4-quantized
  backbone (~5.2 GiB peak, fits an 8 GB GPU). Open Model License — commercial
  use permitted. 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-gr00t_n17-franka_panda-libero_spatial-bf16
  manifest: ./rskill.yaml
  role: s1
  kind: vla
  model_family: gr00t
  embodiment_tags:
    - franka_panda
  actions:
    - pick
    - place
    - open
    - close
  objects:
    - bowl
    - cup
    - drawer
    - object
  scenes:
    - tabletop
  sensors_required:
    - rgb:observation.images.camera1
    - rgb:observation.images.camera2
  state_dim: 8
  action_dim: 7
  action_representation: delta_ee_6d_plus_gripper
  runtime: pytorch
  quantization: bf16/pytorch
  chunk_size: 16
  latency_budget:
    per_chunk_ms: 1500
  license_code: Apache-2.0
  license_weights: nvidia_open_model
  weights_uri: hf://OpenRAL/rskill-gr00t_n17-franka_panda-libero_spatial-bf16
  source_repo: hf://nvidia/GR00T-N1.7-LIBERO
  paper_url: https://arxiv.org/abs/2503.14734

gr00t-n17-libero — 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). NVIDIA Isaac GR00T N1.7 (3B, Cosmos-Reason2-2B VLM backbone) finetuned on the LIBERO benchmark, packaged for OpenRAL. 7-D LIBERO action space (delta end-effector 6-DoF + gripper) over two RGB views. Runs in-process via lerobot 0.6.0's native GrootPolicy with an NF4-quantized backbone (~5.2 GiB peak, fits an 8 GB GPU). Open Model License — commercial use permitted.

Capabilities

• Verbs: pick · place · open · close
• Objects: bowl · cup · drawer · object
• Scenes: tabletop
• Embodiments: franka_panda

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: nvidia_open_model — 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-gr00t_n17-franka_panda-libero_spatial-bf16")
# 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.