| name | rskill-internvla_n1-mobile_base-vln-nf4 |
| description | S1 Vision-Language-Action policy. Capabilities: navigate, reach on room, hallway, door, kitchen, landmark. InternVLA-N1 / DualVLN — a dual-system Vision-Language Navigation foundation model (Qwen2.5-VL-7B waypoint planner + NavDP diffusion controller). Takes egocentric RGB-D plus a natural-language navigation instruction and drives a mobile base with body-twist velocity commands, emitting STOP on arrival. Zero-shot across wheeled and legged bases (paper: Unitree Go2 / H1). 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-internvla_n1-mobile_base-vln-nf4","manifest":"./rskill.yaml","role":"s1","kind":"vla","model_family":"internvla_n1","embodiment_tags":["mobile_base"],"actions":["navigate","reach"],"objects":["room","hallway","door","kitchen","landmark"],"scenes":["indoor","kitchen","home"],"sensors_required":["rgb:observation.images.head"],"action_dim":6,"runtime":"pytorch","quantization":"int4/pytorch","min_vram_gb":{"int4":7,"bf16":16.6},"chunk_size":1,"latency_budget":{"per_chunk_ms":2500,"max_execution_s":300},"license_code":"Apache-2.0","license_weights":"cc-by-nc-sa-4.0","weights_uri":"hf://InternRobotics/InternVLA-N1-DualVLN","source_repo":"hf://InternRobotics/InternVLA-N1-DualVLN","paper_url":"https://arxiv.org/abs/2512.08186"} |
rskill-internvla_n1-mobile_base-vln-nf4 — 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). InternVLA-N1 / DualVLN — a dual-system Vision-Language Navigation foundation model (Qwen2.5-VL-7B waypoint planner + NavDP diffusion controller). Takes egocentric RGB-D plus a natural-language navigation instruction and drives a mobile base with body-twist velocity commands, emitting STOP on arrival. Zero-shot across wheeled and legged bases (paper: Unitree Go2 / H1).
Capabilities
- Verbs: navigate · reach
- Objects: room · hallway · door · kitchen · landmark
- Scenes: indoor · kitchen · home
- Embodiments: mobile_base
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:
cc-by-nc-sa-4.0 — NOT fully permissive. The loader surfaces this posture and enforces the non-commercial guard (OPENRAL_ALLOW_NONCOMMERCIAL=1) where applicable. Commercial use may require a separate upstream agreement. This is third-party weight lineage; OpenRAL's own code is Apache-2.0.
How to actually run it (not via an agent harness)
from openral_rskill import rSkill
skill = rSkill.from_pretrained("OpenRAL/rskill-internvla_n1-mobile_base-vln-nf4")
See rskill.yaml for the authoritative, validated manifest.
