| --- |
| 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: 2.0, fp32: 2.0} |
| chunk_size: 1 |
| latency_budget: {per_chunk_ms: 3000.0} |
| 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`](./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) |
| |
| ```python |
| 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`](./rskill.yaml) for the authoritative, validated manifest. |
| |
