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
LIBERO-GeoMotion
English | 简体中文
A simulation benchmark for geometry-constrained robot manipulation
36 fixed scenes · 9 task families · 6 expert demonstrations
Installation · Verify your installation · Python API · Evaluation · Troubleshooting
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.
LIBERO-GeoMotion was introduced in our paper Large Multimodal Models for Direct Robot Control: GPT-6 as a Beginning. See the GPT-GEAR repository for the accompanying robot control and evaluation framework.
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.
🔧 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:
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:
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 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.
✅ Verify your installation
1. Check installation
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
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:
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:
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.
🐍 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:
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.
📐 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, task_families.json, and task manifest 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/, 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:
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.
🛠️ 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:
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 for the simulation runtime and assets.
New code and documentation use the MIT license; bundled upstream code and assets retain their third-party notices.
📖 Citation
If you use LIBERO-GeoMotion in your research, please cite our paper Large Multimodal Models for Direct Robot Control: GPT-6 as a Beginning:
@misc{zhou2026largemultimodalmodelsdirect,
title={Large Multimodal Models for Direct Robot Control: GPT-6 as a Beginning},
author={Youwei Zhou and Bohan Hou and Yankai Fu and Yaoxu Lyu and Yifan Ye and Shilin Shan and Chen Guo and Jiaming Liu and Ran He and Caifeng Shan and Shanghang Zhang and Jianfei Yang and Zhihe Lu},
year={2026},
eprint={2610.28259},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2610.28259}
}