SKILL.md

metadata

name: 3d-diffuser-actor-rlbench
description: >-
  S1 Vision-Language-Action policy. Capabilities: generalist, open, close, pick,
  place. 3D Diffuser Actor (Ke et al., 2024) — a diffusion policy over
  end-effector keyposes fusing multi-view RGB-D into a 3D scene representation,
  on the RLBench PerAct 18-task benchmark. Shares the out-of-process
  CoppeliaSim/PyRep sidecar with the rlbench scene backend (ADR-0062). MIT code
  + checkpoints. The PerAct checkpoint is loaded verbatim; ships three
  live-verified starter tasks. 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-3d-diffuser-actor-rlbench
  manifest: ./rskill.yaml
  role: s1
  kind: vla
  model_family: diffuser_actor
  embodiment_tags:
    - franka_panda
  actions:
    - generalist
    - open
    - close
    - pick
    - place
  scenes:
    - tabletop
  sensors_required:
    - rgb
  action_dim: 8
  runtime: pytorch
  min_vram_gb:
    bf16: 2
    fp32: 2
  chunk_size: 1
  latency_budget:
    per_chunk_ms: 3000
  license_code: Apache-2.0
  license_weights: mit
  weights_uri: hf://katefgroup/3d_diffuser_actor
  source_repo: hf://katefgroup/3d_diffuser_actor
  paper_url: https://arxiv.org/abs/2402.10885

3d-diffuser-actor-rlbench — 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). 3D Diffuser Actor (Ke et al., 2024) — a diffusion policy over end-effector keyposes fusing multi-view RGB-D into a 3D scene representation, on the RLBench PerAct 18-task benchmark. Shares the out-of-process CoppeliaSim/PyRep sidecar with the rlbench scene backend (ADR-0062). MIT code + checkpoints. The PerAct checkpoint is loaded verbatim; ships three live-verified starter tasks.

Capabilities

• Verbs: generalist · open · close · pick · place
• 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: mit — 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-3d-diffuser-actor-rlbench")
# 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.