chore: publish rSkill OpenRAL/rskill-3d-diffuser-actor-rlbench v0.1.0

README.md

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+<!--
+  rSkill README — 3D Diffuser Actor (RLBench PerAct setup).
+  Discovery + provenance card; mirrors rskill.yaml. ADR-0061.
+-->
+
+# rskill-3d-diffuser-actor-rlbench
+
+3D Diffuser Actor — a diffusion policy over end-effector **keyposes** for RLBench,
+running on the CoppeliaSim/PyRep RLBench benchmark backend (ADR-0061).
+
+## What this skill does
+
+Predicts the next end-effector keypose (position + orientation + gripper) from
+multi-view RGB-D, conditioned on a language instruction. Used to benchmark
+3D/keyframe manipulation on the RLBench **PerAct 18-task** suite. Ships the three
+live-verified starter tasks: `open_drawer`, `meat_off_grill`, `close_jar`.
+
+| Field | Value |
+|---|---|
+| Actions | open, close, pick, place (generalist keyframe policy) |
+| Objects | drawer, grill/meat, jar — (PerAct task objects) |
+| Scenes  | tabletop (RLBench / CoppeliaSim) |
+| Embodiment | franka_panda |
+
+## How it works
+
+3D Diffuser Actor lifts the four RLBench camera RGB-D streams into a 3D point-cloud
+scene token field, attends over it with a relative-position transformer, and runs a
+DDPM diffusion head (100 denoising steps) to denoise an end-effector keypose
+trajectory. Each predicted keypose is executed in RLBench by its sampling-based
+motion planner (`EndEffectorPoseViaPlanning`), then the policy re-observes and
+predicts the next keypose. The policy and the CoppeliaSim/PyRep scene run in an
+out-of-process **py3.10 sidecar** (ZMQ + msgpack); the openral adapter
+(`openral_sim.policies.rlbench_3dda`) forks it transparently.
+
+### Observation → action contract
+
+| dir | key | shape | notes |
+|---|---|---|---|
+| in | `observation.images.{left_shoulder,right_shoulder,wrist,front}` | `(H, W, 3) uint8` | RLBench PerAct cameras, 256×256 |
+| in | `observation.point_clouds.{…}` | `(H, W, 3) float32` | per-camera world-frame point clouds |
+| in | `observation.gripper_pose` | `(7,)` float32 | `[x y z qx qy qz qw]` |
+| out | keyframe action | `(8,)` float32 | `[x y z qx qy qz qw gripper_open]` (world frame) |
+
+## Upstream model / training
+
+Weights are the authors' published RLBench PerAct multi-task checkpoint
+(`diffuser_actor_peract.pth`); loaded verbatim, not retrained. Trained by the
+authors on the PerAct 18-task RLBench demonstrations (multi-view RGB-D + keypose
+supervision).
+
+| Field | Value |
+|---|---|
+| Source repo | [`nickgkan/3d_diffuser_actor`](https://github.com/nickgkan/3d_diffuser_actor) |
+| Weights | [`katefgroup/3d_diffuser_actor`](https://huggingface.co/katefgroup/3d_diffuser_actor) — `diffuser_actor_peract.pth` (168 MB) |
+| Paper | [arxiv:2402.10885](https://arxiv.org/abs/2402.10885) — *3D Diffuser Actor: Policy Diffusion with 3D Scene Representations* |
+| License | mit (code + checkpoints) — commercially permissive |
+| Parameters | ~55 M |
+| Training data | RLBench PerAct 18-task demonstrations |
+
+## Supported robots
+
+| Robot | Scene | Status | Notes |
+|---|---|---|---|
+| franka_panda | RLBench (CoppeliaSim) | ✓ validated | open_drawer 4/4, meat_off_grill 3/3, close_jar solved (8 GB Ada host, 2026-06-19) |
+
+## Sensors required
+
+| key | modality | resolution | dtype |
+|---|---|---|---|
+| `observation.images.left_shoulder` | RGB | 256 × 256 | `uint8` |
+| `observation.images.right_shoulder` | RGB | 256 × 256 | `uint8` |
+| `observation.images.wrist` | RGB | 256 × 256 | `uint8` |
+| `observation.images.front` | RGB | 256 × 256 | `uint8` |
+
+## Manifest summary
+
+| Field | Value |
+|---|---|
+| `name` | `OpenRAL/rskill-3d-diffuser-actor-rlbench` |
+| `version` | `0.1.0` |
+| `license` | `mit` |
+| `role` | `s1` |
+| `model_family` | `diffuser_actor` |
+| `embodiment_tags` | `franka_panda` |
+| `runtime` | `pytorch` |
+| `weights_uri` | `hf://katefgroup/3d_diffuser_actor` |
+| `action_contract.dim` | `8` |
+| `latency_budget.per_chunk_ms` | `3000.0` |
+
+## Reproduction
+
+```bash
+# One-time: provision CoppeliaSim 4.1.0 + PyRep + RLBench@peract + the checkpoint
+# in the py3.10 sidecar venv (see docs/adr/0061-rlbench-benchmark-backend.md).
+openral benchmark scene \
+  --config scenes/benchmark/rlbench_open_drawer.yaml \
+  --rskill rskills/3d-diffuser-actor-rlbench
+```
+
+Inference VRAM peaks ~0.43 GB; runs comfortably on an 8 GB GPU. CoppeliaSim is
+proprietary (free EDU license) and is **never** vendored — it is an
+externally-provisioned dependency (CLAUDE.md §1.9 / ADR-0061).
+
+## Evaluation
+
+[`eval/rlbench.json`](eval/rlbench.json) ships the **live single-episode
+verification** that qualifies this starter PR (`reproduced_locally: true`):
+`open_drawer`, `meat_off_grill`, and `close_jar` each succeed (success_rate
+`1.0`, 3 / 5 / 6 macro-keyposes, ~1.0 s/keypose) on an 8 GB Ada host
+(2026-06-19, seed 0). This is **not** the full official protocol — RLBench /
+PerAct / 3DDA evaluate **25 episodes per task** (seed 0, max 25 keyposes). To
+produce the full artifact and overwrite the `results` block, run the suite
+against the provisioned CoppeliaSim sidecar:
+
+```bash
+openral benchmark run --suite rlbench --rskill rskills/3d-diffuser-actor-rlbench
+```
+
+(`openral benchmark run` is the canonical `RSkillEvalResult` producer — ADR-0009
+PR D.) Per-task paper baselines are reported in Ke et al. (2402.10885, Table 1)
+and are intentionally not transcribed into the artifact to avoid mis-citation.
+
+## License
+
+OpenRAL wrapper files in this repository follow the project Apache-2.0 license.
+The wrapped upstream 3D Diffuser Actor code and released
+`diffuser_actor_peract.pth` checkpoint are MIT-licensed; the manifest therefore
+uses `license: mit` for the consumer-visible weight/runtime posture.
+
+## See also
+
+- `scenes/benchmark/rlbench_open_drawer.yaml`
+- `scenes/benchmark/rlbench_meat_off_grill.yaml`
+- `scenes/benchmark/rlbench_close_jar.yaml`
+- `benchmarks/rlbench.yaml`
+- `docs/adr/0061-rlbench-benchmark-backend.md`

SKILL.md

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+---
+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-0061). 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            # generated discovery view of an rSkill
+  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-0061). 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.

eval/rlbench.json

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+{
+  "_comment": "Live single-episode verification of 3D Diffuser Actor (katefgroup/3d_diffuser_actor, MIT) on three RLBench PerAct tasks, reproduced locally on an 8 GB Ada GPU host (2026-06-19) via the CoppeliaSim/PyRep + 3DDA py3.10 sidecars (ADR-0061). This is the starter-PR proof, NOT the full official protocol: the canonical RLBench/PerAct/3DDA protocol is 25 evaluation episodes per task (seed 0, max 25 macro-keyposes) — run the full suite to overwrite these blocks (see source.reproduction_planned). Per-task paper baselines are reported in Ke et al. 2402.10885 Table 1 and are intentionally NOT transcribed here to avoid mis-citation.",
+  "schema_version": "0.1",
+  "source": {
+    "paper": "3D Diffuser Actor: Policy Diffusion with 3D Scene Representations (Ke et al., 2024)",
+    "arxiv": "https://arxiv.org/abs/2402.10885",
+    "model_variant": "3D Diffuser Actor (PerAct multi-task checkpoint, diffuser_actor_peract.pth)",
+    "evaluated_by": "OpenRAL: openral benchmark scene",
+    "reproduced_locally": true,
+    "reproduction_planned": "Full official protocol (25 episodes/task, seed 0, max 25 keyposes) deferred to a dedicated benchmark session — run `openral benchmark run --suite rlbench --rskill rskills/3d-diffuser-actor-rlbench` against the provisioned CoppeliaSim sidecar and overwrite the results block.",
+    "reproduction_cli": {
+      "description": "ADR-0009 PR D: `openral benchmark run` / `openral benchmark scene` is the canonical producer of RSkillEvalResult JSONs. Requires the externally-provisioned CoppeliaSim 4.1.0 + PyRep + RLBench@peract + 3D Diffuser Actor py3.10 sidecar venv (ADR-0061).",
+      "single_scene_example": "openral benchmark scene --config scenes/benchmark/rlbench_open_drawer.yaml --rskill rskills/3d-diffuser-actor-rlbench --n-episodes 1",
+      "all_suites": "openral benchmark run --suite rlbench --rskill rskills/3d-diffuser-actor-rlbench",
+      "suite_max_steps": 25,
+      "notes": [
+        "CoppeliaSim is proprietary / free-EDU and is NEVER vendored; provision it yourself per ADR-0061.",
+        "The 3D Diffuser Actor checkpoint and code are MIT-licensed — no install-time license guard.",
+        "Inference VRAM peak ~0.43 GB; the policy + RLBench scene share one py3.10 ZMQ sidecar.",
+        "results below are reproduced_locally=true at n_episodes=1 per task (live verification); flip to the full 25-episode protocol via the all_suites command above."
+      ]
+    },
+    "table": null,
+    "status": "reproduced"
+  },
+  "benchmark": {
+    "name": "RLBench",
+    "dataset": null,
+    "protocol": "Live verification: 1 episode per task, seed=0, success_key=is_success, max 25 macro-keyposes/episode (each planned + executed by RLBench EndEffectorPoseViaPlanning). Official PerAct/3DDA protocol is 25 episodes/task.",
+    "robot": "franka_panda",
+    "simulator": "CoppeliaSim 4.1.0 / PyRep (RLBench@peract fork)"
+  },
+  "eval_config": {
+    "n_episodes_per_task": 1,
+    "seeds": [0],
+    "success_key": "is_success",
+    "max_steps": 25,
+    "vla_id": "diffuser_actor",
+    "weights_uri": "hf://katefgroup/3d_diffuser_actor",
+    "denoising_steps": 100,
+    "cameras": ["left_shoulder", "right_shoulder", "wrist", "front"],
+    "observation_size": [256, 256],
+    "action_dim": 8,
+    "inference_vram_gb_peak": 0.43
+  },
+  "results": {
+    "rlbench/open_drawer": {
+      "success_rate": 1.0,
+      "n_episodes": 1,
+      "keyposes": 3,
+      "mean_keypose_latency_ms": 1006.0
+    },
+    "rlbench/meat_off_grill": {
+      "success_rate": 1.0,
+      "n_episodes": 1,
+      "keyposes": 5,
+      "mean_keypose_latency_ms": 974.0
+    },
+    "rlbench/close_jar": {
+      "success_rate": 1.0,
+      "n_episodes": 1,
+      "keyposes": 6,
+      "mean_keypose_latency_ms": 964.0
+    },
+    "avg_success_rate": 1.0,
+    "n_tasks": 3,
+    "n_episodes_per_task": 1,
+    "n_episodes_total": 3
+  },
+  "baselines": {},
+  "trace_id": null
+}

rskill.yaml

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+# rSkill manifest — openral packaging format V1 (CLAUDE.md §6.4)
+# Wraps: katefgroup/3d_diffuser_actor  (diffuser_actor_peract.pth)
+# Paper: Ke et al., 2024 — "3D Diffuser Actor: Policy Diffusion with 3D Scene
+#        Representations" (arXiv:2402.10885). RLBench PerAct 18-task setup.
+#
+# LICENSE: MIT (code + released checkpoints) — commercially permissive. No
+# license guard needed (unlike RVT/RVT-2, which are NVIDIA non-commercial).
+#
+# RUNTIME: auto-managed out-of-process sidecar (ZMQ + msgpack), ADR-0061. The
+# policy AND the CoppeliaSim/PyRep RLBench scene run in their own externally-
+# provisioned py3.10 venv (CoppeliaSim is proprietary, free-EDU, NEVER vendored).
+# The openral adapter (openral_sim.policies.rlbench_3dda) forks
+# tools/rlbench_3dda_sidecar.py on first use; user workflow is one command:
+#
+#   openral benchmark scene --config scenes/benchmark/rlbench_open_drawer.yaml \
+#               --rskill rskills/3d-diffuser-actor-rlbench
+#
+# Verified live on an 8 GB Ada GPU host (2026-06-19): open_drawer 4/4,
+# meat_off_grill 3/3, close_jar solved. Inference VRAM peak ~0.43 GB.
+
+# ── Identity ───────────────────────────────────────────────────────────────
+schema_version: "0.1"
+name: "OpenRAL/rskill-3d-diffuser-actor-rlbench"
+# ADR-0060: the benchmark tasks this checkpoint is validated for (gate). The
+# released PerAct checkpoint covers all 18 PerAct tasks; we ship + declare the
+# three live-verified starter tasks here (the rest are a follow-up).
+evaluated_tasks:
+  - "rlbench/open_drawer"
+  - "rlbench/meat_off_grill"
+  - "rlbench/close_jar"
+version: "0.1.0"
+license: "mit"
+role: "s1"
+kind: "vla"
+
+# ── Policy identity ────────────────────────────────────────────────────────
+model_family: "diffuser_actor"
+
+# ── Compatibility contract ─────────────────────────────────────────────────
+embodiment_tags:
+  - "franka_panda"
+
+# RLBench renders four fixed cameras (the PerAct set: left_shoulder /
+# right_shoulder / wrist / front) and the policy fuses their RGB-D point clouds
+# into a 3D scene representation. Those four are supplied by the SCENE backend
+# (openral_sim.backends.rlbench), NOT by the robot's real sensor list — so the
+# robot-capability gate uses a coarse modality-count requirement ("an RGB-vision
+# embodiment") rather than keyed camera1..4 the franka_panda manifest doesn't
+# declare. The per-camera 3D fusion happens inside the policy sidecar.
+sensors_required:
+  - modality: "rgb"
+    count: 1
+    min_width: 128
+    min_height: 128
+
+# The policy emits next-keyframe end-effector poses; RLBench executes each via
+# its sampling-based motion planner (EndEffectorPoseViaPlanning). Absolute EE
+# pose targets, not deltas.
+actuators_required:
+  - kind: "cartesian_pose"
+    control_mode_semantics:
+      mode: "absolute"
+      reference_frame: "panda_link0"
+
+# ── Runtime / weights ──────────────────────────────────────────────────────
+runtime: "pytorch"
+min_vram_gb:
+  bf16: 2.0
+  fp32: 2.0
+weights_uri: "hf://katefgroup/3d_diffuser_actor"
+
+# ── Execution semantics ────────────────────────────────────────────────────
+# One macro-keypose per step (the scene's mover plans + executes it). 100 DDIM
+# denoising steps per keypose; ~1.2 s/keypose on an 8 GB Ada GPU.
+chunk_size: 1
+latency_budget:
+  per_chunk_ms: 3000.0
+
+# ── IO contract ────────────────────────────────────────────────────────────
+# 8-D keyframe action: [x y z qx qy qz qw gripper_open] (world frame). The scene
+# sidecar appends the peract fork's ignore_collisions channel + plans the motion.
+action_contract:
+  dim: 8
+  slots:
+    - {range: [0, 6], control_mode: "cartesian_pose", ee: "panda_hand", frame: "panda_link0"}
+    - {range: [7, 7], control_mode: "gripper_position", ee: "panda_gripper"}
+
+# ── Provenance ─────────────────────────────────────────────────────────────
+paper_url: "https://arxiv.org/abs/2402.10885"
+source_repo: "hf://katefgroup/3d_diffuser_actor"
+
+description: >
+  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-0061). MIT code + checkpoints. The PerAct
+  checkpoint is loaded verbatim; ships three live-verified starter tasks.
+
+# ADR-0022 — action vocabulary surfaced to the reasoner LLM tool palette.
+actions:
+  - "generalist"
+  - "open"
+  - "close"
+  - "pick"
+  - "place"
+objects: []
+scenes:
+  - "tabletop"