SKILL.md

metadata

name: lingbot-va-galaxea-a1-fruit-placement
description: >-
  S1 Vision-Language-Action policy. Capabilities: pick, place on fruit, mango,
  bowl, plate. LingBot-VA fruit-placement policy for the Galaxea A1. It consumes
  the synchronized front and wrist RGB views, predicts episode-relative EEF pose
  and continuous gripper chunks, and uses the tracked A1 Runtime IK contract
  before OpenRAL validates and executes the resulting joint/gripper actions.
  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-lingbot_va_a1-galaxea_a1-fruit_placement-bf16
  manifest: ./rskill.yaml
  role: s1
  kind: vla
  model_family: lingbot_va_a1
  embodiment_tags:
    - galaxea_a1
  actions:
    - pick
    - place
  objects:
    - fruit
    - mango
    - bowl
    - plate
  scenes:
    - tabletop
  sensors_required:
    - rgb:observation.images.front
    - rgb:observation.images.wrist
  state_dim: 6
  action_dim: 7
  runtime: pytorch
  quantization: bf16/pytorch
  chunk_size: 16
  n_action_steps: 8
  latency_budget:
    per_chunk_ms: 6000
    max_execution_s: 420
  license_code: Apache-2.0
  license_weights: apache-2.0
  weights_uri: >-
    hf://pengyue-polaron/lingbot-va-galaxea-a1-fruit-placement-eef@90e017bdbc6afac2e441b4634c9192776bbcb8b7
  source_repo: hf://robbyant/lingbot-va-base

lingbot-va-galaxea-a1-fruit-placement — 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). LingBot-VA fruit-placement policy for the Galaxea A1. It consumes the synchronized front and wrist RGB views, predicts episode-relative EEF pose and continuous gripper chunks, and uses the tracked A1 Runtime IK contract before OpenRAL validates and executes the resulting joint/gripper actions.

Capabilities

• Verbs: pick · place
• Objects: fruit · mango · bowl · plate
• Scenes: tabletop
• Embodiments: galaxea_a1

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-lingbot_va_a1-galaxea_a1-fruit_placement-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.