chore: publish rSkill OpenRAL/rskill-diffusion-pusht v0.1.0

README.md

@@ -54,7 +54,7 @@ exposes the raw key `observation.image`.
 
 | Field | Value |
 | --- | --- |
-| `name` | `AdrianLlopart/rskill-diffusion-pusht` |
+| `name` | `OpenRAL/rskill-diffusion-pusht` |
 | `version` | `0.1.0` |
 | `license` | `apache-2.0` |
 | `role` | `s1` |
@@ -72,13 +72,13 @@ Full schema: `openral_core.RSkillManifest` —
 ## Reproduction
 
 ```bash
-git clone https://github.com/AdrianLlopart/openral && cd OpenRAL
+git clone https://github.com/OpenRAL/openral && cd OpenRAL
 just bootstrap && uv sync --all-packages --group sim
 
 # End-to-end via the canonical SimEnvironment config (CPU is enough):
 just sim-diffusion-pusht
 # which runs:
-#     ral sim run --config examples/sim/diffusion_pusht.yaml --save-video
+#     openral sim run --config scenes/benchmarks/diffusion_pusht.yaml --save-video
 
 # Sim test (gym_pusht + pymunk):
 uv run pytest tests/sim/test_pusht_2d_diffusion_pusht.py -v -m sim
@@ -93,5 +93,5 @@ match the upstream weights. Commercial use is allowed
 ## See also
 
 - [`robots/pusht_2d/README.md`](../../robots/pusht_2d/README.md) — RobotDescription manifest.
-- [`examples/sim/diffusion_pusht.yaml`](../../examples/sim/diffusion_pusht.yaml) — paired SimEnvironment config.
+- [`scenes/benchmarks/diffusion_pusht.yaml`](../../scenes/benchmarks/diffusion_pusht.yaml) — paired SimEnvironment config.
 - [`docs/reference/vla_compatibility.md`](../../docs/reference/vla_compatibility.md) — VLA × Robot × Sim matrix.

eval/README.md

@@ -3,7 +3,7 @@
 `pusht.json` is the PushT mean-coverage-IoU benchmark result block for this
 rSkill. Validated against
 [`openral_core.RSkillEvalResult`](../../../docs/reference/schemas/RSkillEvalResult.json)
-at load time by the `rSkill` loader and surfaced by `ral benchmark report`.
+at load time by the `rSkill` loader and surfaced by `openral benchmark report`.
 
 | Field | Value |
 | --- | --- |

eval/pusht.json

@@ -1,13 +1,13 @@
 {
-  "schema_version": "1",
+  "schema_version": "0.1",
   "source": {
     "paper": "https://arxiv.org/abs/2303.04137",
     "arxiv": "https://arxiv.org/abs/2303.04137",
     "model_variant": "diffusion",
-    "evaluated_by": "OpenRAL:ral benchmark run",
+    "evaluated_by": "OpenRAL:openral benchmark run",
     "reproduced_locally": true,
     "reproduction_planned": null,
-    "reproduction_cli": "ral benchmark run --suite pusht --rskill rskill://diffusion-pusht",
+    "reproduction_cli": "openral benchmark run --suite pusht --rskill rskill://diffusion-pusht",
     "table": null,
     "status": "reproduced"
   },

rskill.yaml

@@ -2,22 +2,23 @@
 # Wraps: lerobot/diffusion_pusht (Apache-2.0)
 # Paper: Chi et al., 2023 — Diffusion Policy.
 
-schema_version: "1"
-
-name: "AdrianLlopart/rskill-diffusion-pusht"
+# ── Identity ───────────────────────────────────────────────────────────────
+schema_version: "0.1"
+name: "OpenRAL/rskill-diffusion-pusht"
 version: "0.1.0"
 license: "apache-2.0"
 role: "s1"
+kind: "vla"  # ADR-00XX: rSkill kind discriminator. "vla" = learnable Vision-Language-Action policy.
 
+# ── Policy identity ────────────────────────────────────────────────────────
 model_family: "diffusion"
 
+# ── Compatibility contract ─────────────────────────────────────────────────
 # 2-D PushT pseudo-robot (single end-effector pushing a T block). Used by
 # tests/sim/test_pusht_2d_diffusion_pusht.py against gym_pusht/PushT-v0.
 embodiment_tags:
   - "pusht"
 
-capabilities_required: {}
-
 # PushT exposes a single 96×96 RGB top-down stream (named
 # observation.image, not images.cameraN — PushT predates the multi-cam
 # convention used by SmolVLA/ACT).
@@ -32,19 +33,32 @@ sensors_required:
 # `cartesian_pose` as its supported control mode (the codebase
 # convention for the PushT 2-D action regardless of dimensionality).
 # The loader auto-fills n_dof (2) + vla_action_key from the robot YAML.
+# PushT actions are absolute (x, y) targets in the world/scene frame.
 actuators_required:
   - kind: "cartesian_pose"
+    control_mode_semantics:
+      mode: "absolute"
+      reference_frame: "world"
 
+# ── Runtime / weights ──────────────────────────────────────────────────────
 runtime: "pytorch"
-
 quantization:
   dtype: "fp32"
   backend: "pytorch"
-
 weights_uri: "hf://lerobot/diffusion_pusht"
 
-chunk_size: 8
+# ── Preprocessing (all knobs needed to interpret IO) ───────────────────────
+processors:
+  preprocessor_uri: "hf://lerobot/diffusion_pusht/policy_preprocessor.json"
+  postprocessor_uri: "hf://lerobot/diffusion_pusht/policy_postprocessor.json"
+# PushT is a 2-DoF planar pushing benchmark; proprio state is 2-D
+# (x, y) of the end effector.
+state_contract:
+  dim: 2
 
+# ── Execution semantics ────────────────────────────────────────────────────
+chunk_size: 8
+# n_action_steps omitted — equals chunk_size (Diffusion Policy default).
 latency_budget:
   # Reference-host measurement (RTX 4070 Laptop, CUDA 12.8, PyTorch 2.10)
   # of the warm full-chunk inference is 1756 ms — Diffusion Policy runs
@@ -52,21 +66,12 @@ latency_budget:
   # Pinning per_chunk_ms to 1250 ms with tolerance_pct=100 yields the
   # previous 2.5 s ceiling (_WARM_CHUNK_CEILING_S in the sim test).
   per_chunk_ms: 1250.0
-  warmup_ms: 10000.0
-  load_ms: 30000.0
-
-fallback_skill_id: null
 
+# ── Provenance ─────────────────────────────────────────────────────────────
 # Headline success rate from skills/diffusion-pusht/eval/pusht.json.
 benchmarks:
   pusht: 0.60
 
-# PushT is a 2-DoF planar pushing benchmark; proprio state is 2-D
-# (x, y) of the end effector.
-policy_id: "diffusion"
-state_contract:
-  dim: 2
-
 paper_url: "https://arxiv.org/abs/2303.04137"
 source_repo: "hf://lerobot/diffusion_pusht"
 
@@ -76,3 +81,18 @@ description: >
   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.
+
+# ADR-0022 — action vocabulary surfaced to the reasoner LLM tool
+# palette so it can pick this skill by what it does (action verb +
+# object + scene), not just by its slug.
+actions:
+  - "push"
+objects:
+  - "t_shape"
+scenes:
+  - "tabletop_2d"
+
+# ADR-0019 — per-checkpoint action contract (consumed by the dataset bridge
+# to bind the LeRobot v3 `action` feature shape).
+action_contract:
+  dim: 2