Initial release of LIBERO-GeoMotion

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.hdf5 filter=lfs diff=lfs merge=lfs -text
+*.msh filter=lfs diff=lfs merge=lfs -text
+*.obj filter=lfs diff=lfs merge=lfs -text
+*.pruned_init filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+*.py[cod]
+*.egg-info/
+.venv/
+.cache/
+.pytest_cache/
+build/
+dist/
+validation/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 GEoMotion contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

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+include README.zh-CN.md constraints-tested.txt
+recursive-include tools *.py
+recursive-include tests *.py

README.md

@@ -1,3 +1,234 @@
 ---
-license: apache-2.0
+language:
+  - en
+  - zh
+license: other
+license_name: mit-and-upstream-terms
+license_link: https://huggingface.co/datasets/uooon/LIBERO-GeoMotion/blob/main/THIRD_PARTY_NOTICES/NOTICE
+tags:
+  - robotics
+  - simulation
+  - manipulation
+pretty_name: LIBERO-GeoMotion
 ---
+
+<h1 align="center">LIBERO-GeoMotion</h1>
+
+<p align="center"><strong>English</strong> | <a href="README.zh-CN.md">简体中文</a></p>
+
+<p align="center">
+  <strong>A simulation benchmark for geometry-constrained robot manipulation</strong><br>
+  36 fixed scenes · 9 task families · 6 expert demonstrations
+</p>
+
+<p align="center">
+  <a href="#installation">Installation</a> ·
+  <a href="#verify-your-installation">Verify your installation</a> ·
+  <a href="#python-api">Python API</a> ·
+  <a href="#evaluation">Evaluation</a> ·
+  <a href="#troubleshooting">Troubleshooting</a>
+</p>
+
+Welcome to **LIBERO-GeoMotion**! This repository provides a standalone LIBERO-based environment for evaluating robot manipulation under geometric constraints: moving through narrow passages, placing objects with a required orientation, avoiding obstacles, and closing drawers or cabinet doors after storage.
+
+The distribution includes the simulation runtime, scene definitions, required base assets, fixed initial states, and six expert demonstrations. **You can install and run it directly, without a separate LIBERO-plus checkout or asset download.** MuJoCo, robosuite, and other Python dependencies are installed by pip.
+
+Start with one scene to check your installation, then run the full scene checks or connect your own policy through the Python API.
+
+<a id="installation"></a>
+
+## 🔧 Installation
+
+### 1. Prepare the system
+
+| Component | Requirement |
+|---|---|
+| Operating system | Linux; the system package commands below target Ubuntu / Debian |
+| Python | **3.12**; the package requires `>=3.12,<3.13` |
+| Python environment | A dedicated virtual environment to keep simulator dependencies separate |
+| Default rendering | OSMesa for offscreen rendering without a display or GPU |
+| Optional GPU rendering | EGL with a working GPU driver and EGL runtime |
+
+Install the OSMesa and ImageMagick system libraries:
+
+```bash
+sudo apt-get update
+sudo apt-get install -y libosmesa6 libmagickwand-dev
+```
+
+OSMesa provides the default rendering backend; ImageMagick is used by the bundled image-processing code through Wand. These system libraries are required in addition to the Python packages.
+
+The following steps assume `python3.12` is available and supports `venv`. If your distribution packages virtual-environment support separately, install its matching `python3.12-venv` package first.
+
+### 2. Install the environment
+
+Download or clone this repository, open a terminal in its root directory, and run:
+
+```bash
+python3.12 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install -e . -c constraints-tested.txt
+geomotion --help
+```
+
+[`constraints-tested.txt`](constraints-tested.txt) records the validated Linux dependency versions. Passing it with `-c` constrains the packages needed by this project; it does not install every package listed in the file. The editable install (`-e .`) uses the code and assets in this checkout, so keep the repository in place.
+
+In each new terminal, run `source .venv/bin/activate` again from the repository root. This distribution provides the `libero` Python namespace; keep other LIBERO installations in separate environments.
+
+<a id="verify-your-installation"></a>
+
+## ✅ Verify your installation
+
+### 1. Check installation
+
+```bash
+geomotion doctor
+```
+
+The command loads the task suite and reports dependency versions. A successful JSON result contains `"passed": true`, `"tasks": 36`, and reference IDs `[0, 4, 8, 26, 28, 34]`. It does not create a simulation scene or test rendering.
+
+### 2. Run your first scene
+
+```bash
+geomotion smoke --ids 0
+```
+
+This creates task 0, restores its fixed initial state, checks both 256 × 256 camera images, and executes one action. A successful result contains `"passed": true`, `"initial_state_error": 0.0`, and an `elapsed_seconds` value close to `0.05` (20 Hz control).
+
+**No viewer window opens:** the check renders offscreen. `success_after_one_step` reports task completion after that single action; `false` is normal and does not mean the installation failed.
+
+### 3. Check all scenes and replay the experts
+
+Once the first scene works, you can run the broader checks:
+
+```bash
+geomotion smoke --ids all --output validation/smoke.json
+geomotion replay-experts --ids all --output validation/experts.json
+```
+
+The smoke command checks all 36 scenes. Expert replay checks the **six included demonstrations**, verifies their initial states, and executes their recorded actions through the simulator. Each successful replay reports `"success": true`. Replay takes longer than a single smoke check and runs without camera observations.
+
+Use comma-separated IDs for a smaller selection, such as `geomotion smoke --ids 0,4,8`. For `replay-experts`, only IDs `0,4,8,26,28,34` are available. The optional `--output` writes a JSON report after a successful check.
+
+### Optional: use GPU rendering
+
+The CLI defaults to OSMesa. If your machine has a working EGL setup, select it explicitly:
+
+```bash
+geomotion smoke --ids 0 --renderer egl
+```
+
+In Python, use `geomotion.make_env(0, renderer="egl")` before importing MuJoCo or other OpenGL-dependent modules. Switch renderers in a fresh process; in a notebook, restart the kernel first.
+
+<a id="python-api"></a>
+
+## 🐍 Python API
+
+This minimal example loads one scene, prints its instruction and image shapes, executes one stationary command with the gripper open, and closes the environment:
+
+```python
+import geomotion
+
+suite = geomotion.get_suite()
+print("Tasks:", suite.n_tasks)
+print("Instruction:", suite.get_task(0).language)
+
+env, obs = geomotion.make_env(0)
+try:
+    print("Agent view:", obs["agentview_image"].shape)          # (256, 256, 3)
+    print("Wrist view:", obs["robot0_eye_in_hand_image"].shape)  # (256, 256, 3)
+    action = [0, 0, 0, 0, 0, 0, -1]
+    obs, reward, done, info = env.step(action)
+    print("Task success:", bool(env.check_success()))
+finally:
+    env.close()
+```
+
+For policy evaluation, replace the example action with your policy output and repeat for at most **4,000 actions**, stopping as soon as `env.check_success()` is true. The example action only exercises the interface; it is not a task-solving policy.
+
+Actions are seven native `OSC_POSE` control values in `[-1, 1]`: three translation components, three rotation components, and one gripper command (`-1` opens; `+1` closes). Use `geomotion.make_env(task_id)` for each new episode to restore the packaged initial state. Calling bare `env.reset()` does not restore that state.
+
+By default, runtime configuration is written under `~/.cache/libero-geomotion/` (or `$XDG_CACHE_HOME/libero-geomotion/`), in a directory specific to the installation. You can set `config_dir` in `make_env()` to choose another directory. If `LIBERO_CONFIG_PATH` is already set, the runtime uses that directory unless `config_dir` is supplied.
+
+<a id="evaluation"></a>
+
+## 📐 Tasks and evaluation
+
+Task IDs are fixed from **0 to 35**. The published suite is `libero_rp`.
+
+| Task family | Task IDs |
+|---|---|
+| Narrow-Passage Transport | 0–3 |
+| Upright Book Placement | 4–7 |
+| Oriented Bottle Placement | 8–11 |
+| Plate Pushing Around Obstacles | 12–15 |
+| Book Storage in a Drawer | 16–19 |
+| Single-Mug Alignment and Closure | 20–23 |
+| Mango Transport and Avoidance | 24–27 |
+| Package Storage and Drawer Closure | 28–33 |
+| Dual-Mug Alignment and Closure | 34–35 |
+
+The default evaluation settings are:
+
+| Setting | Value |
+|---|---|
+| Robot and controller | Panda, delta `OSC_POSE` |
+| Initialization | Fixed initial state 0, seed 0, no warmup steps |
+| Control frequency | 20 Hz |
+| RGB observations | `agentview` and `robot0_eye_in_hand`, each 256 × 256 |
+| Episode budget | 4,000 native actions |
+| Termination | Stop on `env.check_success()` or the action budget; do not rely on `done` |
+
+Success uses the official task predicates, including placement, orientation, release, stability, door/drawer state, and task-specific process or safety constraints. Simulator geometry, object poses, and predicate internals are evaluator-only information; policy inputs are the task instruction, camera images, robot proprioception, and explicitly selected references.
+
+Report per-scene and per-family success, and distinguish the overall scene mean from the family macro-average because family sizes differ. See the [`evaluation_protocol.json`](geomotion/resources/evaluation_protocol.json), [`task_families.json`](geomotion/resources/task_families.json), and [task manifest](libero_rotation_planning/benchmark/task_manifest.json) for the complete settings and task descriptions.
+
+## 🎬 Expert demonstrations
+
+The suite contains **9 task families: 6 have one reference demonstration each, and 3 have no reference demonstration**. The six demonstrations are bundled under [`geomotion/resources/expert_reference/`](geomotion/resources/expert_reference/), for scene IDs **0, 4, 8, 26, 28, and 34**. Each matches its own test scene's fixed initial state. The 3 families without reference demonstrations are **Plate Pushing Around Obstacles, Book Storage in a Drawer, and Single-Mug Alignment and Closure**. Make the reference selection explicit when reporting evaluation results.
+
+To export the included HDF5 demonstrations into portable videos, text, and trajectories:
+
+```bash
+geomotion export-references --ids all --output ./geomotion-references-256
+```
+
+Each `task_NNN/` directory contains lossless `agentview.mp4` and `wrist.mp4`, each 256 × 256, `videos.json`, `trajectory.json` / `trajectory.jsonl`, and the original expert instructions (`steps.md`). Export renders every saved pre-action state at the evaluation resolution and reconstructs grip-site poses without stepping physics. The final trajectory record has a null action.
+
+Use `--ids 0,4` to export a subset. Existing `task_NNN/` directories are never overwritten; choose a new output directory for another export.
+
+In the packaged HDF5 demonstrations, observations and simulator states precede each paired action; rewards and success flags follow it. Runtime scene loading uses the packaged BDDL and assets. The six files in `expert_reference/text/` contain demonstration steps and measured keypoints, and supply the exported `steps.md`.
+
+<a id="troubleshooting"></a>
+
+## 🛠️ Troubleshooting
+
+| Symptom | What to check |
+|---|---|
+| pip rejects the Python version | Run `python --version` inside the active environment. This release requires Python 3.12. |
+| `geomotion: command not found` | Activate `.venv` and repeat the editable install with that environment's `python -m pip`. You can also use `python -m geomotion.cli --help`. |
+| OSMesa cannot load, or OpenGL initialization fails | Check that `libosmesa6` is installed in the system or container running Python. Try `geomotion smoke --ids 0 --renderer osmesa` in a fresh process. |
+| Wand cannot find ImageMagick / MagickWand | Install `libmagickwand-dev` in the system or container running Python. |
+| EGL initialization fails | Check the GPU driver and EGL availability inside the runtime. Use `--renderer osmesa` to run without GPU rendering. |
+| `renderer is already initialized` or `LIBERO config already initialized` | Start a fresh Python process or restart the notebook kernel, and configure GeoMotion before importing the simulator. |
+| Expert ID is rejected | Replay and export support only `0,4,8,26,28,34`; smoke checks support all IDs from 0 to 35. |
+
+When reporting a problem, include the command you ran, the full traceback, your OS and Python version, the selected renderer, and the `geomotion doctor` output if it runs successfully. This makes installation issues easier to reproduce.
+
+## 📦 Building a wheel (optional)
+
+For an installation that does not depend on the source checkout, build a wheel from the repository root:
+
+```bash
+python -m pip install build
+python -m build
+```
+
+Install the resulting wheel in a fresh Python 3.12 environment with `python -m pip install /path/to/libero_geomotion-0.3.0-py3-none-any.whl`. The wheel includes the packaged scene assets and demonstrations. The system libraries from the installation section are still needed; rerun `geomotion doctor` and `geomotion smoke --ids 0` after installation.
+
+## 🤝 Acknowledgments
+
+We thank the [official LIBERO-Plus repository](https://github.com/sylvestf/LIBERO-plus) for the simulation runtime and assets.
+
+New code and documentation use the [MIT license](LICENSE); bundled upstream code and assets retain their [third-party notices](THIRD_PARTY_NOTICES/NOTICE).

README.zh-CN.md

@@ -0,0 +1,220 @@
+<h1 align="center">LIBERO-GeoMotion</h1>
+
+<p align="center"><a href="README.md">English</a> | <strong>简体中文</strong></p>
+
+<p align="center">
+  <strong>面向几何约束机器人操作的仿真评测环境</strong><br>
+  36 个固定场景 · 9 类任务 · 6 份专家示范
+</p>
+
+<p align="center">
+  <a href="#installation">安装</a> ·
+  <a href="#verify-your-installation">验证安装</a> ·
+  <a href="#python-api">Python API</a> ·
+  <a href="#evaluation">评测约定</a> ·
+  <a href="#troubleshooting">常见问题</a>
+</p>
+
+欢迎使用 **LIBERO-GeoMotion**！这是一个基于 LIBERO 的独立仿真环境，用于评测机器人在几何约束下的操作能力，包括穿过狭窄通道、按指定朝向摆放物体、绕过障碍物，以及收纳物体后关闭抽屉或柜门。
+
+本仓库包含仿真运行代码、场景定义、所需基础资产、固定初态和六份专家示范。**获取仓库后即可安装运行，无需另外下载 LIBERO-plus 仓库或资产。** MuJoCo、robosuite 等 Python 依赖由 pip 安装。
+
+建议先运行一个场景确认安装成功，再检查全部场景，或通过 Python API 接入自己的策略。
+
+<a id="installation"></a>
+
+## 🔧 安装
+
+### 1. 准备系统环境
+
+| 项目 | 要求 |
+|---|---|
+| 操作系统 | Linux；下面的系统包安装命令适用于 Ubuntu / Debian |
+| Python | **3.12**；本包要求 `>=3.12,<3.13` |
+| Python 环境 | 使用独立虚拟环境，隔离仿真依赖 |
+| 默认渲染方式 | OSMesa 离屏渲染，无需显示器或 GPU |
+| 可选 GPU 渲染 | EGL，需要可用的 GPU 驱动和 EGL 运行库 |
+
+安装 OSMesa 和 ImageMagick 系统库：
+
+```bash
+sudo apt-get update
+sudo apt-get install -y libosmesa6 libmagickwand-dev
+```
+
+OSMesa 提供默认渲染后端；仓库中的图像处理代码通过 Wand 使用 ImageMagick。这些系统库需要单独安装，不能只安装 Python 包。
+
+后续步骤假设系统已经提供 `python3.12`，且支持 `venv`。如果你的发行版单独提供虚拟环境支持，请先安装对应的 `python3.12-venv` 包。
+
+### 2. 安装仿真环境
+
+下载或克隆本仓库，在仓库根目录打开终端，然后运行：
+
+```bash
+python3.12 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install -e . -c constraints-tested.txt
+geomotion --help
+```
+
+[`constraints-tested.txt`](constraints-tested.txt) 记录了经过验证的 Linux 依赖版本。`-c` 会约束本项目所需依赖的版本，不会安装文件中列出的所有包。可编辑安装（`-e .`）直接使用当前仓库中的代码和资产，因此安装后请保留仓库目录。
+
+每次打开新终端时，在仓库根目录重新运行 `source .venv/bin/activate`。本发行包提供 `libero` Python 命名空间，请将其他 LIBERO 安装放在不同的虚拟环境中。
+
+<a id="verify-your-installation"></a>
+
+## ✅ 验证安装
+
+### 1. 检查安装
+
+```bash
+geomotion doctor
+```
+
+该命令会加载任务集，并报告依赖版本。成功时，JSON 结果包含 `"passed": true`、`"tasks": 36`，以及示范 ID `[0, 4, 8, 26, 28, 34]`。它不会创建仿真场景，也不会验证渲染。
+
+### 2. 运行第一个场景
+
+```bash
+geomotion smoke --ids 0
+```
+
+该命令会创建任务 0，恢复固定初态，检查两个相机的 256 × 256 图像，并执行一个动作。成功时，结果包含 `"passed": true`、`"initial_state_error": 0.0`，以及接近 `0.05` 的 `elapsed_seconds`（对应 20 Hz 控制频率）。
+
+**运行时不会弹出窗口：** 检查使用离屏渲染。`success_after_one_step` 表示执行这一个动作后是否完成了任务；值为 `false` 是正常的，不代表安装失败。
+
+### 3. 检查全部场景并回放专家示范
+
+第一个场景运行成功后，可以继续执行完整检查：
+
+```bash
+geomotion smoke --ids all --output validation/smoke.json
+geomotion replay-experts --ids all --output validation/experts.json
+```
+
+`smoke` 检查全部 36 个场景。专家回放检查的是**随包提供的六份示范**：验证初态后，在仿真中执行记录的动作。每份示范回放成功时都会报告 `"success": true`。回放比单场景检查耗时更长，且不启用相机观测。
+
+可以用逗号分隔 ID 来缩小范围，例如 `geomotion smoke --ids 0,4,8`。`replay-experts` 仅支持 ID `0,4,8,26,28,34`。可选的 `--output` 会在检查成功后保存 JSON 报告。
+
+### 可选：使用 GPU 渲染
+
+命令行默认使用 OSMesa。如果机器已经配置好 EGL，可以显式选择：
+
+```bash
+geomotion smoke --ids 0 --renderer egl
+```
+
+在 Python 中，先调用 `geomotion.make_env(0, renderer="egl")`，再导入 MuJoCo 或其他依赖 OpenGL 的模块。切换渲染后端时请启动新进程；如果使用 notebook，请先重启内核。
+
+<a id="python-api"></a>
+
+## 🐍 Python API
+
+下面的最小示例会加载一个场景，打印任务指令和图像尺寸，执行一次保持末端���动、夹爪张开的指令，最后关闭环境：
+
+```python
+import geomotion
+
+suite = geomotion.get_suite()
+print("Tasks:", suite.n_tasks)
+print("Instruction:", suite.get_task(0).language)
+
+env, obs = geomotion.make_env(0)
+try:
+    print("Agent view:", obs["agentview_image"].shape)          # (256, 256, 3)
+    print("Wrist view:", obs["robot0_eye_in_hand_image"].shape)  # (256, 256, 3)
+    action = [0, 0, 0, 0, 0, 0, -1]
+    obs, reward, done, info = env.step(action)
+    print("Task success:", bool(env.check_success()))
+finally:
+    env.close()
+```
+
+评测策略时，将示例动作替换为策略输出，最多执行 **4,000 个动作**，并在 `env.check_success()` 为真时立即停止。示例动作仅用于验证接口，不是完成任务的策略。
+
+动作是 `[-1, 1]` 范围内的七维原生 `OSC_POSE` 控制量：三个平移分量、三个旋转分量，以及一个夹爪指令（`-1` 张开，`+1` 闭合）。每个新回合都通过 `geomotion.make_env(task_id)` 恢复随包提供的固定初态；直接调用 `env.reset()` 不会恢复该初态。
+
+运行配置默认保存在 `~/.cache/libero-geomotion/`（或 `$XDG_CACHE_HOME/libero-geomotion/`）下，与当前安装对应的目录中。可以通过 `make_env()` 的 `config_dir` 参数指定其他目录。如果已经设置了 `LIBERO_CONFIG_PATH`，运行时会使用该目录，除非显式传入 `config_dir`。
+
+<a id="evaluation"></a>
+
+## 📐 任务与评测约定
+
+任务 ID 固定为 **0 至 35**，发布的任务集名称为 `libero_rp`。
+
+| 任务类别 | 任务 ID |
+|---|---|
+| 长条穿窄通道 | 0–3 |
+| 书本直立入槽 | 4–7 |
+| 瓶子定向入架 | 8–11 |
+| 盘子绕障推移 | 12–15 |
+| 书本入抽屉并关闭 | 16–19 |
+| 单杯定向入箱并关门 | 20–23 |
+| 芒果搬运与避碰 | 24–27 |
+| 食品盒入抽屉并关闭 | 28–33 |
+| 双杯定向入箱并关门 | 34–35 |
+
+默认评测设置如下：
+
+| 设置 | 默认值 |
+|---|---|
+| 机器人与控制器 | Panda，增量 `OSC_POSE` |
+| 初始化 | 固定初态 0，seed 0，不执行预热步 |
+| 控制频率 | 20 Hz |
+| RGB 观测 | `agentview` 和 `robot0_eye_in_hand`，全程每个视角均为 256 × 256 |
+| 回合预算 | 4,000 个原生动作 |
+| 结束条件 | `env.check_success()` 为真或耗尽动作预算；不依赖 `done` |
+
+任务成功由官方判定逻辑决定，涵盖位置、朝向、释放、稳定放置、门或抽屉状态，以及各任务的过程约束或安全约束。仿真几何真值、物体位姿和判定器内部状态仅供评测端使用；策略输入为任务指令、相机图像、机器人本体状态和显式选择的参考示范。
+
+报告结果时，请给出逐场景与逐类别成功率，并区分整体场景平均和类别宏平均，因为各类别的场景数量不同。完整设置和任务说明见 [`evaluation_protocol.json`](geomotion/resources/evaluation_protocol.json)、[`task_families.json`](geomotion/resources/task_families.json) 和[任务清单](libero_rotation_planning/benchmark/task_manifest.json)。
+
+## 🎬 专家示范
+
+本任务集包含 **9 类任务，其中 6 类各有一份对应的参考示范，另外 3 类暂无参考示范**。六份示范位于 [`geomotion/resources/expert_reference/`](geomotion/resources/expert_reference/)，分别对应场景 ID **0、4、8、26、28、34**，各自与对应测试场景使用相同的固定初态。暂无参考示范的 3 类任务为：**盘子绕障推移、书本入抽屉并关闭、单杯定向入箱并关门**。报告评测结果时，请明确说明参考示范的选择方式。
+
+可以将随包提供的 HDF5 示范导出为视频、文字和轨迹文件：
+
+```bash
+geomotion export-references --ids all --output ./geomotion-references-256
+```
+
+每个 `task_NNN/` 目录包含双视角无损 `agentview.mp4` 和 `wrist.mp4`（每个视角 256 × 256）、`videos.json`、`trajectory.json` / `trajectory.jsonl`，以及原始专家说明 `steps.md`。导出按评测分辨率重新渲染每个动作前状态，同时恢复夹爪控制点位姿，不推进物理仿真。最后一条轨迹记录的动作为空。
+
+使用 `--ids 0,4` 可以只导出部分示范。已有的 `task_NNN/` 目录不会被覆盖；再次导出时请选择新的输出目录。
+
+随包 HDF5 示范中的观测和仿真状态对应每个动作执行前，奖励和成功标记对应动作执行后。运行时使用随包提供的 BDDL 和资产加载场景。`expert_reference/text/` 中的六份文件记录各示范的操作步骤和实测关键点，是导出 `steps.md` 的数据来源。
+
+<a id="troubleshooting"></a>
+
+## 🛠️ 常见问题
+
+| 现象 | 排查方法 |
+|---|---|
+| pip 提示 Python 版本不兼容 | 在当前环境中执行 `python --version`；本版本要求 Python 3.12。 |
+| `geomotion: command not found` | 激活 `.venv`，使用该环境的 `python -m pip` 重新执行可编辑安装。也可以运行 `python -m geomotion.cli --help`。 |
+| 无法加载 OSMesa，或 OpenGL 初始化失��� | 确认运行 Python 的系统或容器内已安装 `libosmesa6`，在新进程中尝试 `geomotion smoke --ids 0 --renderer osmesa`。 |
+| Wand 找不到 ImageMagick / MagickWand | 在运行 Python 的系统或容器内安装 `libmagickwand-dev`。 |
+| EGL 初始化失败 | 检查运行环境内的 GPU 驱动和 EGL 是否可用；可使用 `--renderer osmesa` 在无需 GPU 渲染的情况下运行。 |
+| `renderer is already initialized` 或 `LIBERO config already initialized` | 启动新 Python 进程或重启 notebook 内核，并在导入仿真模块前配置 GeoMotion。 |
+| 专家示范 ID 不受支持 | 回放和导出仅支持 `0,4,8,26,28,34`；场景检查支持 0 至 35 的全部 ID。 |
+
+反馈问题时，请附上执行的命令、完整报错、操作系统和 Python 版本、所选渲染后端，以及 `geomotion doctor` 的输出（如果能够成功运行），方便复现安装问题。
+
+## 📦 构建 wheel（可选）
+
+如果希望安装后不再依赖源码目录，可以在仓库根目录构建 wheel：
+
+```bash
+python -m pip install build
+python -m build
+```
+
+在新的 Python 3.12 环境中执行 `python -m pip install /path/to/libero_geomotion-0.3.0-py3-none-any.whl`，安装生成的 wheel。场景资产和示范会随包提供，安装章节中的系统库仍需准备。安装后重新运行 `geomotion doctor` 和 `geomotion smoke --ids 0`。
+
+## 🤝 致谢
+
+感谢 [LIBERO-Plus 官方仓库](https://github.com/sylvestf/LIBERO-plus) 提供的仿真运行代码与资产支持。
+
+新增代码和文档使用 [MIT 许可证](LICENSE)；随包提供的上游代码与资产保留各自的[第三方声明](THIRD_PARTY_NOTICES/NOTICE)。

THIRD_PARTY_NOTICES/LIBERO_LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Lifelong Robot Learning
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

THIRD_PARTY_NOTICES/NOTICE

@@ -0,0 +1,12 @@
+# Third-party notices
+
+GEoMotion's new code/documentation uses the root MIT license. Do not treat that notice as a replacement copyright or a blanket grant for externally sourced assets.
+
+* LIBERO source: https://github.com/Lifelong-Robot-Learning/LIBERO . Its MIT notice is retained as `LIBERO_LICENSE` (source: https://raw.githubusercontent.com/Lifelong-Robot-Learning/LIBERO/master/LICENSE).
+* LIBERO-Plus simulator: https://github.com/sylvestf/LIBERO-plus . The bundled runtime is based on commit `4976dc30028e805ff8094b55501d532c48fec182`.
+* LIBERO-Plus assets: https://huggingface.co/datasets/Sylvest/LIBERO-plus . The [upstream dataset card](https://huggingface.co/datasets/Sylvest/LIBERO-plus/blob/main/README.md) declares MIT. The source checkout did not include a root license file.
+* Asset sources and geometry adaptations are listed in [asset_provenance.json](asset_provenance.json) and [additional_asset_provenance.json](additional_asset_provenance.json). Decorative books reuse the book mesh; food packages and the white mug are additional upstream-derived assets.
+
+Bundled assets retain their upstream terms. The dataset's MIT declaration does not establish separate license grants from the original mesh and texture suppliers, including scanned/new-object and TurboSquid collections; no supplier-specific licenses were included in the source distribution.
+
+Base arena resources are bundled; robot and simulator-library resources come from the installed robosuite distribution. Python dependencies are installed separately under their own licenses.

THIRD_PARTY_NOTICES/additional_asset_provenance.json

@@ -0,0 +1,57 @@
+[
+  {
+    "category": "rp_mango",
+    "source": "libero/libero/assets/new_objects/half_mango/jywliv/usd/MJCF/jywliv.xml",
+    "scale": [
+      1.0969739087223997,
+      0.5860699401588242,
+      1.3205693562531964
+    ],
+    "method": "Native textured half-mango visual; 130 x 60 x 46 mm rigid ellipsoid collision proxy matching visual bounds. Proxy avoids unstable contacts from tiny scan facets; it approximates the cut surface."
+  },
+  {
+    "category": "rp_plate",
+    "source": "libero/libero/assets/stable_scanned_objects/plate/plate.xml",
+    "scale": [
+      1,
+      1,
+      1
+    ],
+    "center": [
+      0.0,
+      0.0,
+      0.01
+    ],
+    "rotation_xyz_deg": [
+      0,
+      0,
+      0
+    ],
+    "method": "Upstream scanned textured plate visual with a fitted 120 x 120 x 18 mm ellipsoid collision proxy; approximation of the rim and base for planar pushing, not suitable for nesting objects inside the plate."
+  },
+  {
+    "category": "rp_safety_cup",
+    "source": "libero/libero/assets/turbosquid_objects/porcelain_mug/porcelain_mug.xml",
+    "scale": [
+      0.7,
+      0.7,
+      0.7
+    ],
+    "center": [
+      0.011,
+      0.0,
+      0.053
+    ],
+    "rotation_xyz_deg": [
+      0,
+      0,
+      180
+    ],
+    "method": "Upstream textured visual mesh and corresponding collision geometry transformed together; no primitive visual replacement."
+  },
+  {
+    "category": "native_drawer_fixtures",
+    "source": "libero/libero/assets/articulated_objects/wooden_cabinet.xml",
+    "method": "Native textured cabinet, transformed by -90 degrees around Z into the front-negative-X fixture frame. Only the selected drawer slide remains active; narrow insert rails are additional visible collision geometry. Bottom-drawer variants stand on four 80 mm legs."
+  }
+]

THIRD_PARTY_NOTICES/asset_provenance.json

@@ -0,0 +1,62 @@
+[
+  {
+    "category": "rp_book",
+    "source": "libero/libero/assets/new_objects/comic_book/qkczyc/usd/MJCF/qkczyc.xml",
+    "scale": [
+      0.6187964231959303,
+      0.5215919496993368,
+      1.153874979716362
+    ],
+    "center": [
+      4.11495566368103e-05,
+      0.004485875368118286,
+      -2.4493783712387085e-07
+    ],
+    "rotation_xyz_deg": [
+      0,
+      0,
+      0
+    ],
+    "method": "Textured source book mesh with box collision fitted to its visual bounding dimensions."
+  },
+  {
+    "category": "rp_bottle",
+    "source": "libero/libero/assets/turbosquid_objects/wine_bottle/wine_bottle.xml",
+    "scale": [
+      1,
+      1,
+      1
+    ],
+    "center": [
+      0.0,
+      0.0,
+      0.065
+    ],
+    "rotation_xyz_deg": [
+      0,
+      0,
+      0
+    ],
+    "method": "Upstream textured visual mesh and corresponding collision geometry transformed together; no primitive visual replacement."
+  },
+  {
+    "category": "rp_mug",
+    "source": "libero/libero/assets/turbosquid_objects/white_yellow_mug/white_yellow_mug.xml",
+    "scale": [
+      0.7,
+      0.7,
+      0.7
+    ],
+    "center": [
+      -0.018,
+      0.0,
+      0.055
+    ],
+    "rotation_xyz_deg": [
+      0,
+      0,
+      0
+    ],
+    "method": "Upstream textured visual mesh and corresponding collision geometry transformed together; no primitive visual replacement."
+  }
+]

constraints-tested.txt

@@ -0,0 +1,99 @@
+# Python 3.12.13; validated Linux simulation environment.
+absl-py==2.5.1
+attrs==26.1.0
+bddl==1.0.1
+build==1.6.1
+cloudpickle==3.1.2
+contourpy==1.4.0
+cycler==0.12.1
+easydict==1.13
+egl-probe==1.0.2
+etils==1.14.0
+evdev==2.0.0
+fastjsonschema==2.22.2
+filelock==4.2.4
+fonttools==4.67.0
+fsspec==2026.9.0
+future==1.0.0
+glfw==2.11.0
+grpcio==1.84.0
+gym==0.26.2
+gym-notices==0.1.0
+h5py==3.16.0
+imageio==2.38.1
+imageio-ffmpeg==0.6.0
+iniconfig==2.3.1
+jinja2==3.1.6
+jsonschema==4.26.0
+jsonschema-specifications==2025.9.1
+jupyter-core==5.9.1
+jupytext==1.19.6
+kiwisolver==1.5.1
+lazy-loader==0.6
+llvmlite==0.50.0
+markdown==3.11.2
+markdown-it-py==4.2.0
+markupsafe==3.0.4
+matplotlib==3.11.2
+mdit-py-plugins==0.6.1
+mdurl==0.1.2
+mpmath==1.3.0
+mujoco==3.7.0
+nbformat==5.11.1
+networkx==3.7
+numba==0.68.0
+numpy==2.2.6
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cudnn-cu12==9.5.1.17
+nvidia-cufft-cu12==11.3.0.4
+nvidia-cufile-cu12==1.11.1.6
+nvidia-curand-cu12==10.3.7.77
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparselt-cu12==0.6.3
+nvidia-nccl-cu12==2.26.2
+nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvtx-cu12==12.6.77
+opencv-python==5.0.0.93
+opencv-python-headless==4.11.0.86
+packaging==26.3
+pillow==13.0.0
+platformdirs==4.13.5
+pluggy==1.7.0
+protobuf==7.36.2
+psutil==7.2.2
+pygments==2.21.0
+pynput==1.8.2
+pyopengl==3.1.10
+pyparsing==3.3.3
+pyproject-hooks==1.3.3
+pytest==9.1.1
+python-dateutil==2.9.0.post0
+python-xlib==0.33
+pyyaml==6.0.3
+referencing==0.37.0
+robomimic==0.3.0
+robosuite==1.4.1
+rpds-py==2026.9.1
+scikit-image==0.26.0
+scipy==1.18.1
+setuptools==84.2.1
+six==1.17.0
+sympy==1.14.0
+tensorboard==2.21.0
+tensorboard-data-server==0.7.2
+tensorboardx==2.6.5
+termcolor==3.3.0
+tifffile==2026.10.15
+torch==2.7.1
+torchvision==0.22.1
+tqdm==4.70.1
+traitlets==5.16.1
+triton==3.3.1
+typing-extensions==4.16.0
+wand==0.7.2
+werkzeug==3.1.9
+zipp==4.1.1

geomotion/__init__.py

@@ -0,0 +1,59 @@
+"""Portable LIBERO-GeoMotion runtime; importing this module loads no simulator."""
+__version__ = "0.3.0"
+
+from .runtime import evaluation_protocol
+
+_PROTOCOL = evaluation_protocol()
+
+
+def get_suite():
+    from .runtime import configure
+    configure()
+    return _make_suite()
+
+
+def _make_suite():
+    """Construct a fresh suite after the caller has configured the runtime."""
+    from libero_rotation_planning import LIBERO_RP
+    return LIBERO_RP(task_order_index=_PROTOCOL["task_order_index"])
+
+
+def make_env(task_id, *, seed=0, renderer=None, config_dir=None,
+             image_size=_PROTOCOL["camera_resolution"][0],
+             max_steps=_PROTOCOL["max_episode_steps"], camera_obs=True):
+    """Return (env, initial_observation) at fixed state 0, with no physics steps.
+
+    The caller owns env.close() and stops on env.check_success() or max_steps.
+    Create a fresh episode with this factory instead of using env.reset().
+    """
+    from .runtime import configure
+    configure(renderer=renderer, config_dir=config_dir)
+    suite = _make_suite()
+    task_count = suite.n_tasks
+    if type(task_id) is not int or not 0 <= task_id < task_count:
+        raise ValueError(f"task_id must be an integer in 0..{task_count - 1}")
+    if type(seed) is not int or seed < 0:
+        raise ValueError("seed must be a nonnegative integer")
+    if type(image_size) is not int or not 32 <= image_size <= 2048:
+        raise ValueError("image_size must be in 32..2048")
+    if type(max_steps) is not int or max_steps < 1:
+        raise ValueError("max_steps must be positive")
+    from libero.libero.envs import OffScreenRenderEnv
+    env = OffScreenRenderEnv(
+        bddl_file_name=suite.get_task_bddl_file_path(task_id), robots=list(_PROTOCOL["robots"]),
+        controller=_PROTOCOL["controller"], control_freq=_PROTOCOL["control_frequency_hz"],
+        horizon=max_steps, ignore_done=_PROTOCOL["ignore_done"],
+        hard_reset=False, use_camera_obs=camera_obs, has_offscreen_renderer=camera_obs,
+        camera_names=list(_PROTOCOL["camera_names"]),
+        camera_heights=image_size, camera_widths=image_size,
+        camera_depths=False, camera_segmentations=None)
+    try:
+        env.seed(seed)
+        env.env.reset()
+        obs = env.set_init_state(suite.get_task_init_states(task_id)[_PROTOCOL["initial_state_indices"][0]])
+        if env.check_success():
+            raise ValueError("initial state already satisfies task")
+        return env, obs
+    except BaseException:
+        env.close()
+        raise

geomotion/cli.py

@@ -0,0 +1,137 @@
+"""Installation checks, fixed-scene smoke checks, expert replay and reference export."""
+import argparse
+import contextlib
+import importlib.metadata
+import json
+from pathlib import Path
+import sys
+
+from .runtime import RESOURCES, configure, reference_manifest, evaluation_protocol, task_manifest
+
+
+def ids(value, available):
+    selected = list(available) if value == "all" else [int(v) for v in value.split(",")]
+    if not selected or len(set(selected)) != len(selected) or set(selected) - set(available):
+        raise ValueError(f"ids must be unique members of {list(available)}")
+    return selected
+
+
+def doctor():
+    from . import get_suite
+    suite = get_suite()
+    if suite.n_tasks != len(task_manifest()):
+        raise ValueError("task count differs from manifest")
+    return dict(passed=True, tasks=suite.n_tasks,
+                references=[r["id"] for r in reference_manifest()["references"]],
+                packages={p: importlib.metadata.version(p) for p in
+                          ("numpy", "scipy", "torch", "mujoco", "robosuite", "h5py", "PyOpenGL")})
+
+
+def smoke(task_id):
+    import numpy as np
+    from . import get_suite, make_env
+    protocol = evaluation_protocol()
+    env, obs = make_env(task_id)
+    try:
+        initial = get_suite().get_task_init_states(task_id)[protocol["initial_state_indices"][0]]
+        state_error = float(np.max(np.abs(env.sim.get_state().flatten() - initial)))
+        if state_error > 1e-12 or env.check_success():
+            raise ValueError("fixed initial state mismatch")
+        def validate_images(observation):
+            for camera in protocol["camera_names"]:
+                image = observation[camera + "_image"]
+                if image.shape != (*protocol["camera_resolution"], 3) or image.dtype != np.uint8:
+                    raise ValueError("invalid camera observation")
+
+        validate_images(obs)
+        before = float(env.sim.data.time)
+        obs, _, _, _ = env.step([0, 0, 0, 0, 0, 0, -1])
+        validate_images(obs)
+        elapsed = float(env.sim.data.time) - before
+        if not np.isclose(elapsed, 1 / protocol["control_frequency_hz"], atol=1e-9):
+            raise ValueError("incorrect native step frequency")
+        return dict(id=task_id, passed=True, initial_state_error=state_error,
+                    camera_names=protocol["camera_names"],
+                    camera_resolution=protocol["camera_resolution"],
+                    initialization_steps=0, elapsed_seconds=elapsed,
+                    success_after_one_step=bool(env.check_success()))
+    finally:
+        env.close()
+
+
+def replay(task_id):
+    import h5py
+    import numpy as np
+    from . import make_env
+    row = next(r for r in reference_manifest()["references"] if r["id"] == task_id)
+    folder = RESOURCES / "expert_reference"
+    with h5py.File(folder / row["files"]["dataset"]["path"], "r") as f:
+        actions = f["data/demo_0/actions"][:]
+        first = f["data/demo_0/states"][0]
+    env, _ = make_env(task_id, camera_obs=False)
+    try:
+        error = float(np.max(np.abs(first - env.sim.get_state().flatten())))
+        if error > 1e-12:
+            raise ValueError("expert and test initial state differ")
+        first_success = None
+        for i, action in enumerate(actions, 1):
+            env.step(action)
+            if env.check_success() and first_success is None:
+                first_success = i
+        if not env.check_success():
+            raise ValueError(f"expert replay did not succeed: {task_id}")
+        return dict(id=task_id, success=True, actions=len(actions),
+                    first_success_step=first_success, initial_state_error=error)
+    finally:
+        env.close()
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("command", choices=("doctor", "smoke", "replay-experts", "export-references"))
+    parser.add_argument("--ids", default="all")
+    parser.add_argument("--renderer", choices=("osmesa", "egl"), default="osmesa")
+    parser.add_argument("--output", type=Path,
+                        help="required destination directory for export-references; optional JSON report otherwise")
+    args = parser.parse_args(argv)
+    if args.command == "export-references" and args.output is None:
+        parser.error("export-references requires --output DIRECTORY")
+    results = []
+    try:
+        configure(renderer=args.renderer)
+        if args.command == "export-references":
+            from .export_references import convert
+            rows = {r["id"]: r for r in reference_manifest()["references"]}
+            selected = ids(args.ids, rows)
+            output = args.output.expanduser().resolve()
+            for tid in selected:
+                destination = output / f"task_{tid:03d}"
+                if destination.exists() or destination.is_symlink():
+                    raise FileExistsError(f"refusing to overwrite {destination}")
+            for tid in selected:
+                with contextlib.redirect_stdout(sys.stderr):
+                    result = convert(rows[tid], output / f"task_{tid:03d}")
+                print(json.dumps(result), flush=True)
+            return 0
+        if args.command == "doctor":
+            with contextlib.redirect_stdout(sys.stderr):
+                results.append(doctor())
+            print(json.dumps(results[-1]), flush=True)
+        else:
+            available = ([t["id"] for t in task_manifest()] if args.command == "smoke"
+                         else [r["id"] for r in reference_manifest()["references"]])
+            for tid in ids(args.ids, available):
+                with contextlib.redirect_stdout(sys.stderr):
+                    result = smoke(tid) if args.command == "smoke" else replay(tid)
+                results.append(result)
+                print(json.dumps(result), flush=True)
+        if args.output:
+            args.output.parent.mkdir(parents=True, exist_ok=True)
+            args.output.write_text(json.dumps(dict(passed=True, command=args.command, results=results), indent=2) + "\n")
+        return 0
+    except (ValueError, OSError) as exc:
+        parser.exit(1, str(exc) + "\n")
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

geomotion/export_references.py

@@ -0,0 +1,114 @@
+"""Export packaged experts as portable videos, text and quaternion trajectories.
+
+Run: geomotion export-references --output /path/to/references --ids all
+The output uses the existing LIBERO reference protocol consumed by GPT-GEAR.
+"""
+from contextlib import ExitStack
+import json
+import shutil
+import sys
+
+# Published libero-codex-trajectory-v1 protocol; no consumer runtime dependency.
+ACTION_SEMANTICS = dict(
+    meaning='current frame to next frame; last frame is null',
+    dpos='p[t+1] - p[t], metres in environment axes',
+    dquat='q[t+1] * inverse(q[t]), shortest relative wxyz rotation, left multiplication',
+    gripper='actions[t,6]: -1=open, +1=close; null for gaps or unknown commands',
+    execution='reference only; execution parameters are chosen by the consumer',
+)
+
+
+def compact_json(value):
+    return json.dumps(value, ensure_ascii=False, allow_nan=False, separators=(',', ':'))
+
+
+def convert(row, output):
+    import h5py
+    import imageio.v2 as imageio
+    import numpy as np
+    from scipy.spatial.transform import Rotation
+    import geomotion
+    from geomotion.runtime import RESOURCES, evaluation_protocol
+
+    folder = RESOURCES / 'expert_reference'
+    protocol = evaluation_protocol()
+    height, width = protocol['camera_resolution']
+    cameras = {name: ('wrist.mp4' if name == 'robot0_eye_in_hand' else name + '.mp4')
+               for name in protocol['camera_names']}
+    rendering = dict(camera_names=list(cameras), camera_resolution=[height, width],
+                     image_origin='top-left', method='saved_state_render')
+    fps = row['control_hz']
+    output.mkdir(parents=True, exist_ok=False)
+    env = None
+    try:
+        env, _ = geomotion.make_env(row['id'], camera_obs=False)
+        positions, rotations = [], []
+        with h5py.File(folder / row['files']['dataset']['path'], 'r') as h5:
+            data = h5['data/demo_0']
+            count = len(data['actions'])
+            if count < 1 or count != row['num_samples'] or len(data['states']) != count:
+                raise ValueError('expert sample count mismatch')
+            if not np.allclose(env.sim.get_state().flatten(), data['states'][0], rtol=0, atol=1e-12):
+                raise ValueError('expert initial state differs from packaged scene')
+            states = data['states'][:]
+            # Render saved pre-action states at the evaluation resolution. The
+            # source HDF5's historical 128px images cannot supply this detail.
+            with ExitStack() as stack:
+                videos = {}
+                for name, filename in cameras.items():
+                    writer = imageio.get_writer(str(output / filename), fps=fps,
+                        codec='libx264rgb', pixelformat='rgb24', macro_block_size=None,
+                        ffmpeg_params=['-crf', '0', '-threads', '1'])
+                    stack.callback(writer.close)
+                    videos[name] = writer
+                for i in range(count):
+                    env.sim.set_state_from_flattened(states[i])
+                    env.sim.forward()  # reconstruct the recorded state, never replay physics
+                    sid = env.robots[0].eef_site_id
+                    positions.append(env.sim.data.site_xpos[sid].copy())
+                    rotations.append(env.sim.data.site_xmat[sid].copy().reshape(3, 3))
+                    for name, writer in videos.items():
+                        rgb = env.sim.render(width=width, height=height, camera_name=name)
+                        writer.append_data(np.ascontiguousarray(rgb[::-1]))
+                    if (i + 1) % 250 == 0 or i + 1 == count:
+                        print(f"task {row['id']:03d}: rendered {i + 1}/{count}",
+                              file=sys.stderr, flush=True)
+            actions = data['actions'][:]
+        positions = np.asarray(positions)
+        if not all(np.isfinite(v).all() for v in (positions, rotations, actions)):
+            raise ValueError('non-finite expert pose or action')
+        orientation = Rotation.from_matrix(rotations)
+        records = []
+        for i in range(count):
+            action = None
+            if i + 1 < count:
+                xyzw = (orientation[i + 1] * orientation[i].inv()).as_quat()
+                wxyz = xyzw[[3, 0, 1, 2]]
+                if wxyz[0] < 0:
+                    wxyz = -wxyz
+                grip = float(actions[i, 6])
+                action = dict(dpos=(positions[i + 1] - positions[i]).tolist(),
+                              dquat=wxyz.tolist(),
+                              gripper='close' if grip > 0 else 'open' if grip < 0 else None)
+            records.append(dict(frame_index=i, action=action))
+        descriptor = dict(format='libero-codex-trajectory-v1', frames=count, fps=fps,
+            pose=dict(reference='robot0_grip_site', coordinate_frame='environment',
+                      position_unit='metre', quaternion_order='wxyz'),
+            action=ACTION_SEMANTICS,
+            sampling=dict(video='frame_aligned_pre_action', nominal_sample_interval_seconds=1 / fps,
+                          sampling_gaps=[], unknown_gripper_action_frames=[
+                              i for i, r in enumerate(records[:-1]) if r['action']['gripper'] is None]),
+            files=dict(trajectory='trajectory.jsonl', videos=cameras))
+        (output / 'trajectory.jsonl').write_text(''.join(compact_json(r) + '\n' for r in records))
+        (output / 'trajectory.json').write_text(compact_json(descriptor) + '\n')
+        (output / 'videos.json').write_text(compact_json(dict(
+            format='sim-reference-videos-v1', videos=cameras, rendering=rendering)) + '\n')
+        shutil.copyfile(folder / row['files']['text']['path'], output / 'steps.md')
+        return dict(id=row['id'], frames=count, output=str(output), **rendering)
+    except BaseException:
+        shutil.rmtree(output)  # only this newly created, incomplete conversion
+        raise
+    finally:
+        if env is not None:
+            env.close()
+

geomotion/resources/evaluation_protocol.json

@@ -0,0 +1,87 @@
+{
+  "version": "0.3.0",
+  "suite": "libero_rp",
+  "task_order_index": 0,
+  "task_ids": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21,
+    22,
+    23,
+    24,
+    25,
+    26,
+    27,
+    28,
+    29,
+    30,
+    31,
+    32,
+    33,
+    34,
+    35
+  ],
+  "initial_state_indices": [
+    0
+  ],
+  "control_frequency_hz": 20,
+  "max_episode_steps": 4000,
+  "robots": [
+    "Panda"
+  ],
+  "controller": "OSC_POSE",
+  "action_dimension": 7,
+  "action_range": [
+    -1,
+    1
+  ],
+  "camera_names": [
+    "agentview",
+    "robot0_eye_in_hand"
+  ],
+  "camera_resolution": [
+    256,
+    256
+  ],
+  "policy_inputs": [
+    "task language",
+    "agentview RGB",
+    "wrist RGB",
+    "robot proprioception"
+  ],
+  "excluded_policy_inputs": [
+    "scene geometry truth",
+    "object poses",
+    "expert controller internals",
+    "success-predicate internal state"
+  ],
+  "success": "env.check_success(), including stable placement and sticky process/safety rules",
+  "report": [
+    "per-instance success",
+    "per-task-type success",
+    "overall instance mean"
+  ],
+  "reference_max_steps": 3519,
+  "note": "Fixed-instance initial benchmark; no empirically calibrated difficulty labels. Expert actions can include conservative holds and are not time-optimal.",
+  "ignore_done": true,
+  "episode_termination": "External evaluation loop: stop on env.check_success() or max_episode_steps."
+}

geomotion/resources/expert_reference/actions/rp3_028_cream_cheese_drawer_v0_actions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc077a9c33b664df98c3acc093c3c26f054b894cc089e1d95b356452496cca1b
+size 154856

geomotion/resources/expert_reference/actions/rp3_034_two_mugs_v0_actions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f179e6840bbd16f228a7dd46fcf03a53e5e74c304cea89323d75e038a6299a64
+size 148752

geomotion/resources/expert_reference/actions/rp_008_bar_gate_v0_actions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:670f364633c91c9ff810dc32d3cffe2dfc819c637a082946b9593e082441e61c
+size 109328

geomotion/resources/expert_reference/actions/rp_012_book_slot_v0_actions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07ccf05f5829aa562a52b61a5144cec0cf5c3921c3be94bc2d640e3b806b7367
+size 33112

geomotion/resources/expert_reference/actions/rp_016_bottle_shelf_v0_actions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:929b872857dd52704f7d42564433734c83c108f7951d31e0665534ad8209e848
+size 78360

geomotion/resources/expert_reference/actions/rp_050_mango_safety_yaw90_layout0_actions.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccb40c37e708289120e00ea0f9e7cb60fcc8471faf9ba7da8dda72a34a21320a
+size 52488

geomotion/resources/expert_reference/dataset/rp3_028_cream_cheese_drawer_v0_demo.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:231370b83a581848ee7dcd350e4558838405049a6e9b8257199fab9e72ce794e
+size 33591727

geomotion/resources/expert_reference/dataset/rp3_034_two_mugs_v0_demo.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3369ec9561cd2f8b6014a86d866af339fe7dff3cbae069378981449e7d99f039
+size 39433757

geomotion/resources/expert_reference/dataset/rp_008_bar_gate_v0_demo.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb2c7ef42f58808bf40a069b97b0a6aab2ef4ecdf5aa927f6aff484616f3a01f
+size 18431631

geomotion/resources/expert_reference/dataset/rp_012_book_slot_v0_demo.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbac04dc60cc3587d55e39246d211a32c1fc9480bd21ea92cfe95c779a073364
+size 10745259

geomotion/resources/expert_reference/dataset/rp_016_bottle_shelf_v0_demo.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:057e1cc0d10d9907c0b73e6a9da83a0457833a9c46d8811ce56b0bf1cf8a4b8b
+size 21205538

geomotion/resources/expert_reference/dataset/rp_050_mango_safety_yaw90_layout0_demo.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5551d4b00ad36df3ed6096cec68cdd18819f8a0f081540fd8f9ec573ca94e28
+size 15288260

geomotion/resources/expert_reference/reference_manifest.json

@@ -0,0 +1,154 @@
+{
+  "references": [
+    {
+      "id": 0,
+      "name": "rp_008_bar_gate_v0",
+      "language": "put the blue bar on the green pad",
+      "label_zh": "把蓝色长条放到绿色垫子上",
+      "num_samples": 1950,
+      "control_hz": 20,
+      "files": {
+        "dataset": {
+          "path": "dataset/rp_008_bar_gate_v0_demo.hdf5"
+        },
+        "video": {
+          "path": "video/rp_008_bar_gate_v0_replay.mp4"
+        },
+        "actions": {
+          "path": "actions/rp_008_bar_gate_v0_actions.npy"
+        },
+        "measured_trajectory_csv": {
+          "path": "trajectory/rp_008_bar_gate_v0_eef.csv"
+        },
+        "text": {
+          "path": "text/rp_008_bar_gate_v0.md"
+        }
+      }
+    },
+    {
+      "id": 4,
+      "name": "rp_012_book_slot_v0",
+      "language": "put the book upright in the organizer",
+      "label_zh": "把书本直立放入收纳槽",
+      "num_samples": 589,
+      "control_hz": 20,
+      "files": {
+        "dataset": {
+          "path": "dataset/rp_012_book_slot_v0_demo.hdf5"
+        },
+        "video": {
+          "path": "video/rp_012_book_slot_v0_replay.mp4"
+        },
+        "actions": {
+          "path": "actions/rp_012_book_slot_v0_actions.npy"
+        },
+        "measured_trajectory_csv": {
+          "path": "trajectory/rp_012_book_slot_v0_eef.csv"
+        },
+        "text": {
+          "path": "text/rp_012_book_slot_v0.md"
+        }
+      }
+    },
+    {
+      "id": 8,
+      "name": "rp_016_bottle_shelf_v0",
+      "language": "place the bottle in the rack with its cap facing the opening and its body parallel to the long sides",
+      "label_zh": "竖瓶放倒并定向放入托架",
+      "num_samples": 1397,
+      "control_hz": 20,
+      "files": {
+        "dataset": {
+          "path": "dataset/rp_016_bottle_shelf_v0_demo.hdf5"
+        },
+        "video": {
+          "path": "video/rp_016_bottle_shelf_v0_replay.mp4"
+        },
+        "actions": {
+          "path": "actions/rp_016_bottle_shelf_v0_actions.npy"
+        },
+        "measured_trajectory_csv": {
+          "path": "trajectory/rp_016_bottle_shelf_v0_eef.csv"
+        },
+        "text": {
+          "path": "text/rp_016_bottle_shelf_v0.md"
+        }
+      }
+    },
+    {
+      "id": 26,
+      "name": "rp_050_mango_safety_yaw90_layout0",
+      "language": "pick up the mango half and place it on the green pad without knocking over or moving the vase and nearby cup",
+      "label_zh": "保护易倒物体的半块芒果抓取与绕行",
+      "num_samples": 935,
+      "control_hz": 20,
+      "files": {
+        "dataset": {
+          "path": "dataset/rp_050_mango_safety_yaw90_layout0_demo.hdf5"
+        },
+        "video": {
+          "path": "video/rp_050_mango_safety_yaw90_layout0_replay.mp4"
+        },
+        "actions": {
+          "path": "actions/rp_050_mango_safety_yaw90_layout0_actions.npy"
+        },
+        "measured_trajectory_csv": {
+          "path": "trajectory/rp_050_mango_safety_yaw90_layout0_eef.csv"
+        },
+        "text": {
+          "path": "text/rp_050_mango_safety_yaw90_layout0.md"
+        }
+      }
+    },
+    {
+      "id": 28,
+      "name": "rp3_028_cream_cheese_drawer_v0",
+      "language": "put the cream cheese package in the top drawer organizer and close the drawer",
+      "label_zh": "打开上层抽屉收纳奶油奶酪盒后关闭",
+      "num_samples": 2763,
+      "control_hz": 20,
+      "files": {
+        "dataset": {
+          "path": "dataset/rp3_028_cream_cheese_drawer_v0_demo.hdf5"
+        },
+        "video": {
+          "path": "video/rp3_028_cream_cheese_drawer_v0_replay.mp4"
+        },
+        "actions": {
+          "path": "actions/rp3_028_cream_cheese_drawer_v0_actions.npy"
+        },
+        "measured_trajectory_csv": {
+          "path": "trajectory/rp3_028_cream_cheese_drawer_v0_eef.csv"
+        },
+        "text": {
+          "path": "text/rp3_028_cream_cheese_drawer_v0.md"
+        }
+      }
+    },
+    {
+      "id": 34,
+      "name": "rp3_034_two_mugs_v0",
+      "language": "put the yellow and white mug in the storage box with its handle facing the back, put the white mug beside it with its handle facing the opening, and close the door",
+      "label_zh": "双杯把手朝相反方向入柜并关门",
+      "num_samples": 2654,
+      "control_hz": 20,
+      "files": {
+        "dataset": {
+          "path": "dataset/rp3_034_two_mugs_v0_demo.hdf5"
+        },
+        "video": {
+          "path": "video/rp3_034_two_mugs_v0_replay.mp4"
+        },
+        "actions": {
+          "path": "actions/rp3_034_two_mugs_v0_actions.npy"
+        },
+        "measured_trajectory_csv": {
+          "path": "trajectory/rp3_034_two_mugs_v0_eef.csv"
+        },
+        "text": {
+          "path": "text/rp3_034_two_mugs_v0.md"
+        }
+      }
+    }
+  ]
+}

geomotion/resources/expert_reference/replay_experts.py

@@ -0,0 +1,5 @@
+"""Standalone compatibility entry; use geomotion replay-experts."""
+import sys
+from geomotion.cli import main
+if __name__ == "__main__":
+    raise SystemExit(main(["replay-experts", *sys.argv[1:]]))

geomotion/resources/expert_reference/text/rp3_028_cream_cheese_drawer_v0.md

@@ -0,0 +1,77 @@
+# 任务 28：打开上层抽屉收纳奶油奶酪盒后关闭 · 布局 B 专家
+
+任务指令：`put the cream cheese package in the top drawer organizer and close the drawer`
+
+本轨迹从布局 B 的固定初态实际执行，独立复位回放和渲染回放均成功。共 **2763 步 / 138.15 秒**，动作频率 20 Hz。
+
+## 专家过程
+
+操作抽屉（需要时先打开）；抓取并调整物体姿态；对齐收纳槽并放入、松手；退出后关闭抽屉并等待稳定。
+
+下面列出本次实际执行的关键点；末端位置与夹爪命令对应每段结束时的记录。完整逐步实测轨迹见 CSV，动作、仿真状态及双视角观测见 HDF5。CSV 的末端/关节数据位于动作前，成功标记位于动作后；关键点表的末端位置位于该段动作结束后。
+
+| 动作步范围（0 起） | 秒 | 操作 | 实测末端 XYZ（m） | 夹爪命令 |
+| --- | --- | --- | --- | --- |
+| 0–106 | 0.00–5.35 | 抽屉操作与收纳 | -0.2237, 0.1523, 1.2036 | 张开 |
+| 107–152 | 5.35–7.65 | 抽屉操作与收纳 | -0.1458, 0.1530, 1.0859 | 张开 |
+| 153–174 | 7.65–8.75 | 抽屉操作与收纳 | -0.0859, 0.1528, 1.0838 | 张开 |
+| 175–214 | 8.75–10.75 | 抽屉操作与收纳 | -0.0836, 0.1527, 1.0844 | 闭合 |
+| 215–266 | 10.75–13.35 | 抽屉操作与收纳 | -0.2363, 0.1525, 1.0849 | 闭合 |
+| 267–301 | 13.35–15.10 | 抽屉操作与收纳 | -0.2364, 0.1526, 1.0849 | 张开 |
+| 302–461 | 15.10–23.10 | 抽屉操作与收纳 | -0.3283, 0.1700, 1.2274 | 张开 |
+| 462–570 | 23.10–28.55 | 抽屉操作与收纳 | -0.2001, -0.1189, 1.3199 | 张开 |
+| 571–646 | 28.55–32.35 | 抓取与提起 | -0.1882, -0.2252, 1.1221 | 张开 |
+| 647–806 | 32.35–40.35 | 抓取与提起 | -0.1852, -0.2279, 0.9490 | 张开 |
+| 807–841 | 40.35–42.10 | 抓取与提起 | -0.1849, -0.2279, 0.9490 | 闭合 |
+| 842–900 | 42.10–45.05 | 抓取与提起 | -0.1889, -0.2256, 1.1176 | 闭合 |
+| 901–981 | 45.05–49.10 | 抽屉操作与收纳 | -0.1896, -0.2100, 1.0180 | 闭合 |
+| 982–1021 | 49.10–51.10 | 抽屉操作与收纳 | -0.1891, -0.2089, 1.0194 | 张开 |
+| 1022–1074 | 51.10–53.75 | 抽屉操作与收纳 | -0.1892, -0.2087, 1.1774 | 张开 |
+| 1075–1177 | 53.75–58.90 | 抽屉操作与收纳 | -0.2002, -0.1190, 1.3197 | 张开 |
+| 1178–1254 | 58.90–62.75 | 抓取与提起 | -0.1873, -0.2036, 1.1203 | 张开 |
+| 1255–1321 | 62.75–66.10 | 抓取与提起 | -0.1879, -0.2031, 0.9143 | 张开 |
+| 1322–1351 | 66.10–67.60 | 抓取与提起 | -0.1878, -0.2033, 0.9117 | 闭合 |
+| 1352–1422 | 67.60–71.15 | 抓取与提起 | -0.1883, -0.2025, 1.1173 | 闭合 |
+| 1423–1551 | 71.15–77.60 | 抽屉操作与收纳 | -0.1141, 0.1451, 1.2494 | 闭合 |
+| 1552–1571 | 77.60–78.60 | 闭环对齐物体 | -0.1118, 0.1461, 1.1903 | 闭合 |
+| 1572–1591 | 78.60–79.60 | 闭环对齐物体 | -0.1139, 0.1456, 1.1245 | 闭合 |
+| 1592–1606 | 79.60–80.35 | 闭环对齐物体 | -0.1126, 0.1462, 1.1003 | 闭合 |
+| 1607–1621 | 80.35–81.10 | 闭环对齐物体 | -0.1123, 0.1464, 1.1003 | 闭合 |
+| 1622–1636 | 81.10–81.85 | 闭环对齐物体 | -0.1123, 0.1466, 1.1003 | 闭合 |
+| 1637–1651 | 81.85–82.60 | 闭环对齐物体 | -0.1123, 0.1467, 1.1003 | 闭合 |
+| 1652–1666 | 82.60–83.35 | 闭环对齐物体 | -0.1123, 0.1469, 1.1003 | 闭合 |
+| 1667–1681 | 83.35–84.10 | 闭环对齐物体 | -0.1123, 0.1470, 1.1003 | 闭合 |
+| 1682–1696 | 84.10–84.85 | 闭环对齐物体 | -0.1123, 0.1472, 1.1003 | 闭合 |
+| 1697–1711 | 84.85–85.60 | 闭环对齐物体 | -0.1124, 0.1473, 1.1003 | 闭合 |
+| 1712–1726 | 85.60–86.35 | 闭环对齐物体 | -0.1124, 0.1474, 1.1003 | 闭合 |
+| 1727–1741 | 86.35–87.10 | 闭环对齐物体 | -0.1124, 0.1475, 1.1003 | 闭合 |
+| 1742–1756 | 87.10–87.85 | 闭环对齐物体 | -0.1124, 0.1476, 1.1003 | 闭合 |
+| 1757–1771 | 87.85–88.60 | 闭环对齐物体 | -0.1124, 0.1477, 1.1003 | 闭合 |
+| 1772–1786 | 88.60–89.35 | 闭环对齐物体 | -0.1124, 0.1478, 1.1003 | 闭合 |
+| 1787–1801 | 89.35–90.10 | 闭环对齐物体 | -0.1124, 0.1479, 1.1003 | 闭合 |
+| 1802–1816 | 90.10–90.85 | 闭环对齐物体 | -0.1124, 0.1479, 1.1003 | 闭合 |
+| 1817–1831 | 90.85–91.60 | 闭环对齐物体 | -0.1125, 0.1480, 1.1003 | 闭合 |
+| 1832–1846 | 91.60–92.35 | 闭环对齐物体 | -0.1125, 0.1481, 1.1003 | 闭合 |
+| 1847–1861 | 92.35–93.10 | 闭环对齐物体 | -0.1125, 0.1482, 1.1003 | 闭合 |
+| 1862–1876 | 93.10–93.85 | 闭环对齐物体 | -0.1125, 0.1482, 1.1003 | 闭合 |
+| 1877–1891 | 93.85–94.60 | 闭环对齐物体 | -0.1125, 0.1482, 1.1003 | 闭合 |
+| 1892–1906 | 94.60–95.35 | 闭环对齐物体 | -0.1125, 0.1483, 1.1003 | 闭合 |
+| 1907–1921 | 95.35–96.10 | 闭环对齐物体 | -0.1125, 0.1483, 1.1003 | 闭合 |
+| 1922–1956 | 96.10–97.85 | 抽屉操作与收纳 | -0.1125, 0.1482, 1.1003 | 张开 |
+| 1957–2019 | 97.85–101.00 | 抽屉操作与收纳 | -0.1129, 0.1483, 1.2878 | 张开 |
+| 2020–2075 | 101.00–103.80 | 抽屉操作与收纳 | -0.2710, 0.1523, 1.2638 | 闭合 |
+| 2076–2138 | 103.80–106.95 | 抽屉操作与收纳 | -0.2720, 0.1527, 1.0957 | 闭合 |
+| 2139–2588 | 106.95–129.45 | 抽屉操作与收纳 | -0.0852, 0.1500, 1.0673 | 闭合 |
+| 2589–2635 | 129.45–131.80 | 抽屉操作与收纳 | -0.2125, 0.1501, 1.0882 | 闭合 |
+| 2636–2722 | 131.80–136.15 | 抽屉操作与收纳 | -0.2132, 0.1501, 1.2857 | 张开 |
+| 2723–2762 | 136.15–138.15 | 释放后等待稳定 | -0.2131, 0.1500, 1.2859 | 张开 |
+
+## 对应数据
+
+- [HDF5](../dataset/rp3_028_cream_cheese_drawer_v0_demo.hdf5)
+- [7 维动作 NPY](../actions/rp3_028_cream_cheese_drawer_v0_actions.npy)
+- [实测末端与关节轨迹 CSV](../trajectory/rp3_028_cream_cheese_drawer_v0_eef.csv)
+- [双视角回放 MP4](../video/rp3_028_cream_cheese_drawer_v0_replay.mp4)
+
+
+控制来源：`privileged_scripted_waypoints_osc`。

geomotion/resources/expert_reference/text/rp3_034_two_mugs_v0.md

@@ -0,0 +1,73 @@
+# 任务 34：双杯把手朝相反方向入柜并关门 · 布局 B 专家
+
+任务指令：`put the yellow and white mug in the storage box with its handle facing the back, put the white mug beside it with its handle facing the opening, and close the door`
+
+本轨迹从布局 B 的固定初态实际执行，独立复位回放和渲染回放均成功。共 **2654 步 / 132.70 秒**，动作频率 20 Hz。
+
+## 专家过程
+
+抓取杯子并提起；对齐对应杯把目标方向；逐个放入柜内并释放；退出后关闭柜门。
+
+下面列出本次实际执行的关键点；末端位置与夹爪命令对应每段结束时的记录。完整逐步实测轨迹见 CSV，动作、仿真状态及双视角观测见 HDF5。CSV 的末端/关节数据位于动作前，成功标记位于动作后；关键点表的末端位置位于该段动作结束后。
+
+| 动作步范围（0 起） | 秒 | 操作 | 实测末端 XYZ（m） | 夹爪命令 |
+| --- | --- | --- | --- | --- |
+| 0–74 | 0.00–3.75 | 杯子定向收纳 | -0.2004, -0.1386, 1.3482 | 张开 |
+| 75–175 | 3.75–8.80 | 抓取与提起 | -0.2480, -0.3441, 1.1215 | 张开 |
+| 176–225 | 8.80–11.30 | 抓取与提起 | -0.2483, -0.3459, 0.9706 | 张开 |
+| 226–255 | 11.30–12.80 | 抓取与提起 | -0.2480, -0.3462, 0.9690 | 闭合 |
+| 256–309 | 12.80–15.50 | 抓取与提起 | -0.2488, -0.3451, 1.1177 | 闭合 |
+| 310–394 | 15.50–19.75 | 杯子定向收纳 | -0.2494, -0.3447, 1.3551 | 闭合 |
+| 395–554 | 19.75–27.75 | 放置与释放 | -0.0044, 0.0516, 1.1684 | 闭合 |
+| 555–610 | 27.75–30.55 | 放置与释放 | -0.0012, 0.0554, 0.9891 | 闭合 |
+| 611–640 | 30.55–32.05 | 放置与释放 | -0.0008, 0.0553, 0.9891 | 张开 |
+| 641–700 | 32.05–35.05 | 放置与释放 | -0.0004, 0.0561, 1.1679 | 张开 |
+| 701–808 | 35.05–40.45 | 杯子定向收纳 | -0.1985, -0.1384, 1.3487 | 张开 |
+| 809–921 | 40.45–46.10 | 抓取与提起 | 0.0393, -0.2527, 1.1202 | 张开 |
+| 922–974 | 46.10–48.75 | 抓取与提起 | 0.0419, -0.2537, 0.9677 | 张开 |
+| 975–1004 | 48.75–50.25 | 抓取与提起 | 0.0420, -0.2537, 0.9657 | 闭合 |
+| 1005–1061 | 50.25–53.10 | 抓取与提起 | 0.0408, -0.2536, 1.1177 | 闭合 |
+| 1062–1281 | 53.10–64.10 | 杯子定向收纳 | 0.0378, -0.2528, 1.3527 | 闭合 |
+| 1282–1441 | 64.10–72.10 | 放置与释放 | 0.0109, 0.1691, 1.1853 | 闭合 |
+| 1442–1501 | 72.10–75.10 | 放置与释放 | -0.0021, 0.2025, 0.9895 | 闭合 |
+| 1502–1531 | 75.10–76.60 | 放置与释放 | -0.0016, 0.2030, 0.9888 | 张开 |
+| 1532–1591 | 76.60–79.60 | 放置与释放 | -0.0016, 0.2039, 1.1677 | 张开 |
+| 1592–1751 | 79.60–87.60 | 关闭柜门 | -0.3865, -0.0866, 1.2191 | 闭合 |
+| 1752–1911 | 87.60–95.60 | 关闭柜门 | -0.3751, -0.1326, 1.0866 | 闭合 |
+| 1912–1936 | 95.60–96.85 | 关闭柜门 | -0.3749, -0.1298, 1.0869 | 闭合 |
+| 1937–1961 | 96.85–98.10 | 关闭柜门 | -0.3757, -0.1241, 1.0870 | 闭合 |
+| 1962–1986 | 98.10–99.35 | 关闭柜门 | -0.3788, -0.1100, 1.0869 | 闭合 |
+| 1987–2011 | 99.35–100.60 | 关闭柜门 | -0.3821, -0.0887, 1.0868 | 闭合 |
+| 2012–2036 | 100.60–101.85 | 关闭柜门 | -0.3837, -0.0604, 1.0866 | 闭合 |
+| 2037–2061 | 101.85–103.10 | 关闭柜门 | -0.3806, -0.0254, 1.0866 | 闭合 |
+| 2062–2086 | 103.10–104.35 | 关闭柜门 | -0.3697, 0.0160, 1.0868 | 闭合 |
+| 2087–2111 | 104.35–105.60 | 关闭柜门 | -0.3542, 0.0510, 1.0871 | 闭合 |
+| 2112–2136 | 105.60–106.85 | 关闭柜门 | -0.3393, 0.0746, 1.0873 | 闭合 |
+| 2137–2161 | 106.85–108.10 | 关闭柜门 | -0.3268, 0.0906, 1.0873 | 闭合 |
+| 2162–2186 | 108.10–109.35 | 关闭柜门 | -0.3160, 0.1026, 1.0873 | 闭合 |
+| 2187–2211 | 109.35–110.60 | 关闭柜门 | -0.3056, 0.1124, 1.0873 | 闭合 |
+| 2212–2236 | 110.60–111.85 | 关闭柜门 | -0.2949, 0.1212, 1.0873 | 闭合 |
+| 2237–2261 | 111.85–113.10 | 关闭柜门 | -0.2839, 0.1292, 1.0873 | 闭合 |
+| 2262–2286 | 113.10–114.35 | 关闭柜门 | -0.2724, 0.1364, 1.0872 | 闭合 |
+| 2287–2311 | 114.35–115.60 | 关闭柜门 | -0.2606, 0.1428, 1.0872 | 闭合 |
+| 2312–2336 | 115.60–116.85 | 关闭柜门 | -0.2484, 0.1484, 1.0871 | 闭合 |
+| 2337–2361 | 116.85–118.10 | 关闭柜门 | -0.2360, 0.1532, 1.0871 | 闭合 |
+| 2362–2386 | 118.10–119.35 | 关闭柜门 | -0.2199, 0.1576, 1.0871 | 闭合 |
+| 2387–2411 | 119.35–120.60 | 关闭柜门 | -0.2040, 0.1610, 1.0871 | 闭合 |
+| 2412–2436 | 120.60–121.85 | 关闭柜门 | -0.1892, 0.1631, 1.0871 | 闭合 |
+| 2437–2461 | 121.85–123.10 | 关闭柜门 | -0.1868, 0.1640, 1.0868 | 闭合 |
+| 2462–2486 | 123.10–124.35 | 关闭柜门 | -0.1858, 0.1649, 1.0868 | 闭合 |
+| 2487–2511 | 124.35–125.60 | 关闭柜门 | -0.1831, 0.1658, 1.0869 | 闭合 |
+| 2512–2546 | 125.60–127.35 | 关闭柜门 | -0.2812, 0.1657, 1.0868 | 闭合 |
+| 2547–2613 | 127.35–130.70 | 关闭柜门 | -0.2816, 0.1659, 1.2850 | 张开 |
+| 2614–2653 | 130.70–132.70 | 释放后等待稳定 | -0.2813, 0.1659, 1.2853 | 张开 |
+
+## 对应数据
+
+- [HDF5](../dataset/rp3_034_two_mugs_v0_demo.hdf5)
+- [7 维动作 NPY](../actions/rp3_034_two_mugs_v0_actions.npy)
+- [实测末端与关节轨迹 CSV](../trajectory/rp3_034_two_mugs_v0_eef.csv)
+- [双视角回放 MP4](../video/rp3_034_two_mugs_v0_replay.mp4)
+
+
+控制来源：`privileged_scripted_waypoints_osc`。

geomotion/resources/expert_reference/text/rp_008_bar_gate_v0.md

@@ -0,0 +1,47 @@
+# 任务 00：把蓝色长条放到绿色垫子上 · 布局 B 专家
+
+任务指令：`put the blue bar on the green pad`
+
+本轨迹从布局 B 的固定初态实际执行，独立复位回放和渲染回放均成功。共 **1950 步 / 97.50 秒**，动作频率 20 Hz。
+
+## 专家过程
+
+接近长条并夹起；调整朝向后暂放、从端部换抓；穿过狭口；在绿色垫子上释放并退出。
+
+下面列出本次实际执行的关键点；末端位置与夹爪命令对应每段结束时的记录。完整逐步实测轨迹见 CSV，动作、仿真状态及双视角观测见 HDF5。CSV 的末端/关节数据位于动作前，成功标记位于动作后；关键点表的末端位置位于该段动作结束后。
+
+| 动作步范围（0 起） | 秒 | 操作 | 实测末端 XYZ（m） | 夹爪命令 |
+| --- | --- | --- | --- | --- |
+| 0–76 | 0.00–3.85 | 抓取与提起 | -0.1615, 0.1083, 1.1204 | 张开 |
+| 77–107 | 3.85–5.40 | 抓取与提起 | -0.1619, 0.1081, 1.0286 | 张开 |
+| 108–137 | 5.40–6.90 | 抓取与提起 | -0.1621, 0.1083, 1.0260 | 闭合 |
+| 138–170 | 6.90–8.55 | 抓取与提起 | -0.1629, 0.1078, 1.1175 | 闭合 |
+| 171–247 | 8.55–12.40 | 暂放/换抓/穿过狭口 | -0.1510, 0.1194, 1.2271 | 闭合 |
+| 248–308 | 12.40–15.45 | 暂放/换抓/穿过狭口 | -0.1510, 0.1197, 1.0342 | 闭合 |
+| 309–343 | 15.45–17.20 | 暂放/换抓/穿过狭口 | -0.1507, 0.1200, 1.0318 | 张开 |
+| 344–401 | 17.20–20.10 | 暂放/换抓/穿过狭口 | -0.1508, 0.1199, 1.2054 | 张开 |
+| 402–651 | 20.10–32.60 | 暂放/换抓/穿过狭口 | -0.3007, 0.1240, 1.2384 | 张开 |
+| 652–730 | 32.60–36.55 | 暂放/换抓/穿过狭口 | -0.3059, 0.1201, 1.0302 | 张开 |
+| 731–759 | 36.55–38.00 | 暂放/换抓/穿过狭口 | -0.2382, 0.1196, 1.0274 | 张开 |
+| 760–799 | 38.00–40.00 | 暂放/换抓/穿过狭口 | -0.2358, 0.1195, 1.0274 | 闭合 |
+| 800–852 | 40.00–42.65 | 暂放/换抓/穿过狭口 | -0.2358, 0.1194, 1.1664 | 闭合 |
+| 853–865 | 42.65–43.30 | 暂放/换抓/穿过狭口 | -0.2055, 0.1189, 1.1570 | 闭合 |
+| 866–918 | 43.30–45.95 | 暂放/换抓/穿过狭口 | -0.0576, 0.1194, 1.1532 | 闭合 |
+| 919–984 | 45.95–49.25 | 暂放/换抓/穿过狭口 | 0.1331, 0.1389, 1.1534 | 闭合 |
+| 985–1028 | 49.25–51.45 | 暂放/换抓/穿过狭口 | 0.1341, 0.1422, 1.0470 | 闭合 |
+| 1029–1029 | 51.45–51.50 | 暂放/换抓/穿过狭口 | 0.1342, 0.1421, 1.0471 | 张开 |
+| 1030–1209 | 51.50–60.50 | 暂放/换抓/穿过狭口 | 0.0941, 0.1426, 1.0611 | 张开 |
+| 1210–1409 | 60.50–70.50 | 暂放/换抓/穿过狭口 | 0.0953, 0.1431, 1.0594 | 张开 |
+| 1410–1659 | 70.50–83.00 | 暂放/换抓/穿过狭口 | 0.0936, 0.1421, 1.0632 | 张开 |
+| 1660–1909 | 83.00–95.50 | 暂放/换抓/穿过狭口 | 0.0460, 0.1189, 1.1787 | 张开 |
+| 1910–1949 | 95.50–97.50 | 释放后等待稳定 | 0.0459, 0.1188, 1.1786 | 张开 |
+
+## 对应数据
+
+- [HDF5](../dataset/rp_008_bar_gate_v0_demo.hdf5)
+- [7 维动作 NPY](../actions/rp_008_bar_gate_v0_actions.npy)
+- [实测末端与关节轨迹 CSV](../trajectory/rp_008_bar_gate_v0_eef.csv)
+- [双视角回放 MP4](../video/rp_008_bar_gate_v0_replay.mp4)
+
+
+控制来源：`privileged_scripted_waypoints_osc`。

geomotion/resources/expert_reference/text/rp_012_book_slot_v0.md

@@ -0,0 +1,38 @@
+# 任务 04：把书本直立放入收纳槽 · 布局 B 专家
+
+任务指令：`put the book upright in the organizer`
+
+本轨迹从布局 B 的固定初态实际执行，独立复位回放和渲染回放均成功。共 **589 步 / 29.45 秒**，动作频率 20 Hz。
+
+## 专家过程
+
+对齐书本抓取方向并提起；翻转为直立姿态；闭环对齐收纳槽；降低、释放并退出。
+
+下面列出本次实际执行的关键点；末端位置与夹爪命令对应每段结束时的记录。完整逐步实测轨迹见 CSV，动作、仿真状态及双视角观测见 HDF5。CSV 的末端/关节数据位于动作前，成功标记位于动作后；关键点表的末端位置位于该段动作结束后。
+
+| 动作步范围（0 起） | 秒 | 操作 | 实测末端 XYZ（m） | 夹爪命令 |
+| --- | --- | --- | --- | --- |
+| 0–69 | 0.00–3.50 | 抓取与提起 | -0.1933, -0.2043, 1.1204 | 张开 |
+| 70–136 | 3.50–6.85 | 抓取与提起 | -0.1932, -0.2060, 0.9154 | 张开 |
+| 137–166 | 6.85–8.35 | 抓取与提起 | -0.1930, -0.2070, 0.9137 | 闭合 |
+| 167–236 | 8.35–11.85 | 抓取与提起 | -0.1940, -0.2056, 1.1172 | 闭合 |
+| 237–354 | 11.85–17.75 | 翻转并入槽 | 0.0197, 0.0644, 1.1801 | 闭合 |
+| 355–374 | 17.75–18.75 | 闭环对齐物体 | 0.0212, 0.0660, 1.1186 | 闭合 |
+| 375–389 | 18.75–19.50 | 闭环对齐物体 | 0.0248, 0.0634, 1.1005 | 闭合 |
+| 390–404 | 19.50–20.25 | 闭环对齐物体 | 0.0240, 0.0644, 1.1010 | 闭合 |
+| 405–424 | 20.25–21.25 | 闭环对齐物体 | 0.0213, 0.0663, 1.0392 | 闭合 |
+| 425–439 | 21.25–22.00 | 闭环对齐物体 | 0.0246, 0.0633, 1.0044 | 闭合 |
+| 440–454 | 22.00–22.75 | 闭环对齐物体 | 0.0242, 0.0642, 1.0050 | 闭合 |
+| 455–489 | 22.75–24.50 | 翻转并入槽 | 0.0239, 0.0644, 1.0050 | 张开 |
+| 490–548 | 24.50–27.45 | 翻转并入槽 | 0.0241, 0.0644, 1.1828 | 张开 |
+| 549–588 | 27.45–29.45 | 释放后等待稳定 | 0.0239, 0.0646, 1.1828 | 张开 |
+
+## 对应数据
+
+- [HDF5](../dataset/rp_012_book_slot_v0_demo.hdf5)
+- [7 维动作 NPY](../actions/rp_012_book_slot_v0_actions.npy)
+- [实测末端与关节轨迹 CSV](../trajectory/rp_012_book_slot_v0_eef.csv)
+- [双视角回放 MP4](../video/rp_012_book_slot_v0_replay.mp4)
+
+
+控制来源：`privileged_scripted_waypoints_osc`。

geomotion/resources/expert_reference/text/rp_016_bottle_shelf_v0.md

@@ -0,0 +1,42 @@
+# 任务 08：竖瓶放倒并定向放入托架 · 布局 B 专家
+
+任务指令：`place the bottle in the rack with its cap facing the opening and its body parallel to the long sides`
+
+本轨迹从布局 B 的固定初态实际执行，独立复位回放和渲染回放均成功。共 **1397 步 / 69.85 秒**，动作频率 20 Hz。
+
+## 专家过程
+
+夹起竖立瓶子；在空区将瓶子放倒；重新抓取并校正瓶盖方向；放入托架、释放并退出。
+
+下面列出本次实际执行的关键点；末端位置与夹爪命令对应每段结束时的记录。完整逐步实测轨迹见 CSV，动作、仿真状态及双视角观测见 HDF5。CSV 的末端/关节数据位于动作前，成功标记位于动作后；关键点表的末端位置位于该段动作结束后。
+
+| 动作步范围（0 起） | 秒 | 操作 | 实测末端 XYZ（m） | 夹爪命令 |
+| --- | --- | --- | --- | --- |
+| 0–83 | 0.00–4.20 | 抓取与提起 | -0.1470, -0.2161, 1.1207 | 张开 |
+| 84–113 | 4.20–5.70 | 抓取与提起 | -0.1478, -0.2161, 1.0339 | 张开 |
+| 114–143 | 5.70–7.20 | 抓取与提起 | -0.1480, -0.2161, 1.0319 | 闭合 |
+| 144–175 | 7.20–8.80 | 抓取与提起 | -0.1490, -0.2156, 1.1177 | 闭合 |
+| 176–425 | 8.80–21.30 | 瓶子放倒与定向 | -0.2923, -0.1429, 1.0177 | 闭合 |
+| 426–465 | 21.30–23.30 | 瓶子放倒与定向 | -0.2920, -0.1418, 1.0177 | 张开 |
+| 466–665 | 23.30–33.30 | 瓶子放倒与定向 | -0.3212, -0.0598, 1.0370 | 张开 |
+| 666–705 | 33.30–35.30 | 释放后等待稳定 | -0.3203, -0.0578, 1.0363 | 张开 |
+| 706–826 | 35.30–41.35 | 瓶子放倒与定向 | -0.1807, -0.1212, 1.2976 | 张开 |
+| 827–893 | 41.35–44.70 | 抓取与提起 | -0.2492, -0.1231, 1.1217 | 张开 |
+| 894–955 | 44.70–47.80 | 抓取与提起 | -0.2496, -0.1230, 0.9305 | 张开 |
+| 956–985 | 47.80–49.30 | 抓取与提起 | -0.2509, -0.1234, 0.9272 | 闭合 |
+| 986–1053 | 49.30–52.70 | 抓取与提起 | -0.2509, -0.1232, 1.1176 | 闭合 |
+| 1054–1175 | 52.70–58.80 | 放置与释放 | -0.0033, 0.1180, 1.1684 | 闭合 |
+| 1176–1250 | 58.80–62.55 | 放置与释放 | 0.0004, 0.1193, 0.9410 | 闭合 |
+| 1251–1280 | 62.55–64.05 | 放置与释放 | -0.0012, 0.1192, 0.9390 | 张开 |
+| 1281–1356 | 64.05–67.85 | 放置与释放 | -0.0019, 0.1194, 1.1681 | 张开 |
+| 1357–1396 | 67.85–69.85 | 释放后等待稳定 | -0.0015, 0.1195, 1.1684 | 张开 |
+
+## 对应数据
+
+- [HDF5](../dataset/rp_016_bottle_shelf_v0_demo.hdf5)
+- [7 维动作 NPY](../actions/rp_016_bottle_shelf_v0_actions.npy)
+- [实测末端与关节轨迹 CSV](../trajectory/rp_016_bottle_shelf_v0_eef.csv)
+- [双视角回放 MP4](../video/rp_016_bottle_shelf_v0_replay.mp4)
+
+
+控制来源：`privileged_scripted_waypoints_osc`。

geomotion/resources/expert_reference/text/rp_050_mango_safety_yaw90_layout0.md

@@ -0,0 +1,38 @@
+# 任务 26：保护易倒物体的半块芒果抓取与绕行 · 布局 B 专家
+
+任务指令：`pick up the mango half and place it on the green pad without knocking over or moving the vase and nearby cup`
+
+本轨迹从布局 B 的固定初态实际执行，独立复位回放和渲染回放均成功。共 **935 步 / 46.75 秒**，动作频率 20 Hz。
+
+## 专家过程
+
+抓取半块芒果；从保护物体侧边绕行搬运；在绿色垫子上释放；退出并检查保护物体的全程安全记录。
+
+下面列出本次实际执行的关键点；末端位置与夹爪命令对应每段结束时的记录。完整逐步实测轨迹见 CSV，动作、仿真状态及双视角观测见 HDF5。CSV 的末端/关节数据位于动作前，成功标记位于动作后；关键点表的末端位置位于该段动作结束后。
+
+| 动作步范围（0 起） | 秒 | 操作 | 实测末端 XYZ（m） | 夹爪命令 |
+| --- | --- | --- | --- | --- |
+| 0–75 | 0.00–3.80 | 抓取与提起 | -0.1370, -0.1864, 1.1208 | 张开 |
+| 76–138 | 3.80–6.95 | 抓取与提起 | -0.1381, -0.1859, 0.9289 | 张开 |
+| 139–168 | 6.95–8.45 | 抓取与提起 | -0.1384, -0.1862, 0.9266 | 闭合 |
+| 169–236 | 8.45–11.85 | 抓取与提起 | -0.1389, -0.1856, 1.1179 | 闭合 |
+| 237–294 | 11.85–14.75 | 绕行搬运 | -0.2690, -0.0819, 1.1717 | 闭合 |
+| 295–400 | 14.75–20.05 | 绕行搬运 | -0.2709, 0.2383, 1.1728 | 闭合 |
+| 401–650 | 20.05–32.55 | 绕行搬运 | 0.0949, 0.2783, 1.1676 | 闭合 |
+| 651–710 | 32.55–35.55 | 放置与释放 | 0.1023, 0.2001, 1.1717 | 闭合 |
+| 711–783 | 35.55–39.20 | 放置与释放 | 0.0978, 0.1995, 0.9352 | 闭合 |
+| 784–813 | 39.20–40.70 | 放置与释放 | 0.1011, 0.2002, 0.9380 | 张开 |
+| 814–894 | 40.70–44.75 | 放置与释放 | 0.0982, 0.2001, 1.1678 | 张开 |
+| 895–934 | 44.75–46.75 | 释放后等待稳定 | 0.0988, 0.2002, 1.1683 | 张开 |
+
+## 对应数据
+
+- [HDF5](../dataset/rp_050_mango_safety_yaw90_layout0_demo.hdf5)
+- [7 维动作 NPY](../actions/rp_050_mango_safety_yaw90_layout0_actions.npy)
+- [实测末端与关节轨迹 CSV](../trajectory/rp_050_mango_safety_yaw90_layout0_eef.csv)
+- [双视角回放 MP4](../video/rp_050_mango_safety_yaw90_layout0_replay.mp4)
+
+
+安全检查：保护物体未发生倾倒或下落，安全违规记录为空。
+
+控制来源：`privileged_scripted_waypoints_osc`。

geomotion/resources/expert_reference/video/rp3_028_cream_cheese_drawer_v0_replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2035eb23a73f2b29c819df9ab3fac399b29881bf5f36c4c28f154b8b8697c97
+size 649472

geomotion/resources/expert_reference/video/rp3_034_two_mugs_v0_replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e4f06f5fd00a3dfaf6285823b26aefa6bd41e17cc1fa628d3d8f0a45017e512
+size 820438

geomotion/resources/expert_reference/video/rp_008_bar_gate_v0_replay.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:832b140412e45e2d8547a78de389db2cdd0dfff8b220cd140a2fadc88597e0b3
+size 409495

geomotion/resources/expert_reference/video/rp_012_book_slot_v0_replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7219e37fa23af7ca50f4d4ea3f57c0549401cabb65d03bf1647a017716ae9ac0
+size 196619

geomotion/resources/expert_reference/video/rp_016_bottle_shelf_v0_replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ceb420c20aacc3bfe94081ad95dfb00d93f59bf0e59dcc106f55d2fa913857e
+size 403715

geomotion/resources/expert_reference/video/rp_050_mango_safety_yaw90_layout0_replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd4c116775848ca93910d78f509b7c7b7032a72b62c44f8a2c20b45b0de911f6
+size 257048

geomotion/resources/task_families.json

@@ -0,0 +1,110 @@
+[
+  {
+    "panel": "a",
+    "name": "Narrow-Passage Transport",
+    "name_zh": "长条穿窄通道",
+    "task_ids": [
+      0,
+      1,
+      2,
+      3
+    ],
+    "count": 4
+  },
+  {
+    "panel": "b",
+    "name": "Upright Book Placement",
+    "name_zh": "书本直立入槽",
+    "task_ids": [
+      4,
+      5,
+      6,
+      7
+    ],
+    "count": 4
+  },
+  {
+    "panel": "c",
+    "name": "Oriented Bottle Placement",
+    "name_zh": "瓶子定向入架",
+    "task_ids": [
+      8,
+      9,
+      10,
+      11
+    ],
+    "count": 4
+  },
+  {
+    "panel": "d",
+    "name": "Plate Pushing Around Obstacles",
+    "name_zh": "盘子绕障推移",
+    "task_ids": [
+      12,
+      13,
+      14,
+      15
+    ],
+    "count": 4
+  },
+  {
+    "panel": "e",
+    "name": "Book Storage in a Drawer",
+    "name_zh": "书本入抽屉并关闭",
+    "task_ids": [
+      16,
+      17,
+      18,
+      19
+    ],
+    "count": 4
+  },
+  {
+    "panel": "f",
+    "name": "Single-Mug Alignment and Closure",
+    "name_zh": "单杯定向入箱并关门",
+    "task_ids": [
+      20,
+      21,
+      22,
+      23
+    ],
+    "count": 4
+  },
+  {
+    "panel": "g",
+    "name": "Mango Transport and Avoidance",
+    "name_zh": "芒果搬运与避碰",
+    "task_ids": [
+      24,
+      25,
+      26,
+      27
+    ],
+    "count": 4
+  },
+  {
+    "panel": "h",
+    "name": "Package Storage and Drawer Closure",
+    "name_zh": "食品盒入抽屉并关闭",
+    "task_ids": [
+      28,
+      29,
+      30,
+      31,
+      32,
+      33
+    ],
+    "count": 6
+  },
+  {
+    "panel": "i",
+    "name": "Dual-Mug Alignment and Closure",
+    "name_zh": "双杯定向入箱并关门",
+    "task_ids": [
+      34,
+      35
+    ],
+    "count": 2
+  }
+]

geomotion/runtime.py

@@ -0,0 +1,59 @@
+"""Resource and renderer setup, before LIBERO or OpenGL is imported."""
+import hashlib
+import json
+import os
+from pathlib import Path
+import sys
+import tempfile
+
+ROOT = Path(__file__).resolve().parent.parent
+RESOURCES = Path(__file__).resolve().parent / "resources"
+
+
+def configure(*, renderer=None, config_dir=None):
+    selected = renderer or os.environ.get("MUJOCO_GL", "osmesa")
+    if selected not in ("osmesa", "egl"):
+        raise ValueError("renderer must be osmesa or egl")
+    if "mujoco" in sys.modules and os.environ.get("MUJOCO_GL") != selected:
+        raise RuntimeError("renderer is already initialized; use a fresh process")
+    os.environ["MUJOCO_GL"] = os.environ["PYOPENGL_PLATFORM"] = selected
+    for name in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
+        if not os.environ.get(name, '').isdigit() or int(os.environ[name]) < 1:
+            os.environ[name] = "1"
+    base = ROOT / "libero/libero"
+    extension = ROOT / "libero_rotation_planning"
+    paths = dict(benchmark_root=str(base), assets=str(base / "assets"),
+                 bddl_files=str(extension / "bddl_files"),
+                 init_states=str(extension / "init_files"),
+                 datasets=str(RESOURCES / "expert_reference/dataset"))
+    cache = Path(os.environ.get("XDG_CACHE_HOME", Path.home() / ".cache"))
+    default = cache / "libero-geomotion" / hashlib.sha256(str(ROOT).encode()).hexdigest()[:16]
+    target = Path(config_dir or os.environ.get("LIBERO_CONFIG_PATH", default)).resolve()
+    existing_config = getattr(sys.modules.get("libero.libero"), "config_file", None)
+    if existing_config is not None:
+        existing = Path(existing_config).resolve()
+        if existing != target / "config.yaml":
+            raise RuntimeError("LIBERO config already initialized; use a fresh process")
+    target.mkdir(parents=True, exist_ok=True)
+    contents = json.dumps(paths, indent=2) + "\n"
+    path = target / "config.yaml"
+    if not path.exists() or path.read_text() != contents:
+        with tempfile.NamedTemporaryFile(mode="w", dir=target, delete=False) as stream:
+            stream.write(contents)
+            temporary = stream.name
+        os.replace(temporary, path)
+    os.environ["LIBERO_CONFIG_PATH"] = str(target)
+    return paths
+
+
+def reference_manifest():
+    return json.loads((RESOURCES / "expert_reference/reference_manifest.json").read_text())
+
+
+def evaluation_protocol():
+    return json.loads((RESOURCES / "evaluation_protocol.json").read_text())
+
+
+def task_manifest():
+    path = ROOT / "libero_rotation_planning/benchmark/task_manifest.json"
+    return json.loads(path.read_text())

libero/__init__.py

@@ -0,0 +1 @@
+"""Bundled LIBERO runtime namespace for the standalone GeoMotion distribution."""