SKILL.md

metadata

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
  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, 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)

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 for the authoritative, validated manifest.