| --- |
| name: diffusion-pusht |
| description: >- |
| S1 Vision-Language-Action policy. Capabilities: push on t_shape. Diffusion Policy (~263M-param U-Net with 100-step DDPM denoiser) for the PushT 2-DoF pushing benchmark. Action chunks of length 8 within a horizon of 16. The chunk inference cost is dominated by the denoising loop, so cached pops are essentially free — this is the extreme test of the queue-drain contract. 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-diffusion-pusht-pusht-fp32 |
| manifest: ./rskill.yaml |
| role: s1 |
| kind: vla |
| model_family: diffusion |
| embodiment_tags: [pusht] |
| actions: [push] |
| objects: [t_shape] |
| scenes: [tabletop_2d] |
| sensors_required: ['rgb:observation.image'] |
| state_dim: 2 |
| action_dim: 2 |
| action_representation: joint_positions |
| runtime: pytorch |
| quantization: fp32/pytorch |
| chunk_size: 8 |
| latency_budget: {per_chunk_ms: 1250.0} |
| license_code: Apache-2.0 |
| license_weights: apache-2.0 |
| weights_uri: hf://lerobot/diffusion_pusht |
| source_repo: hf://lerobot/diffusion_pusht |
| paper_url: https://arxiv.org/abs/2303.04137 |
| --- |
| |
| # diffusion-pusht — 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`). Diffusion Policy (~263M-param U-Net with 100-step DDPM denoiser) for the PushT 2-DoF pushing benchmark. Action chunks of length 8 within a horizon of 16. The chunk inference cost is dominated by the denoising loop, so cached pops are essentially free — this is the extreme test of the queue-drain contract. |
|
|
| ## Capabilities |
|
|
| - **Verbs:** push |
| - **Objects:** t_shape |
| - **Scenes:** tabletop_2d |
| - **Embodiments:** pusht |
|
|
| ## 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) |
|
|
| ```python |
| from openral_rskill import rSkill |
| |
| skill = rSkill.from_pretrained("OpenRAL/rskill-diffusion-pusht-pusht-fp32") |
| # 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. |
|
|
