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# DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation
[Haozhe Xie](https://haozhexie.com), [Beichen Wen](https://wenbc21.github.io/), Jiarui Zheng, [Zhaoxi Chen](https://frozenburning.github.io/), [Fangzhou Hong](https://hongfz16.github.io/), [Haiwen Diao](https://paranioar.github.io/), [Ziwei Liu](https://liuziwei7.github.io/)
S-Lab, Nanyang Technological University
[](https://www.codefactor.io/repository/github/hzxie/DynamicVLA)
[](https://arxiv.org/abs/2601.22153)
[](https://youtu.be/NmJnHcI04_Q)
[](https://huggingface.co/datasets/hzxie/DOM)
## Changelog🔥
- [2026/04/26] Released training and testing code.
- [2026/01/26] Repository created.
## Cite this work📝
```
@inproceedings{xie2026dynamicvla,
title = {{DynamicVLA:} A Vision-Language-Action Model for Dynamic Object Manipulation},
author = {Xie, Haozhe and
Wen, Beichen and
Zheng, Jiarui and
Chen, Zhaoxi and
Hong, Fangzhou and
Diao, Haiwen and
Liu, Ziwei},
booktitle = {NeurIPS},
year = {2026}
}
```
## Dataset and Pretrained Models 🛢️
### DOM Dataset
- [DOM Training Set](https://huggingface.co/datasets/hzxie/DOM) – for training DynamicVLA
- [DOM Testing Set](https://gateway.infinitescript.com/?f=DOM-Test) – for benchmarking; includes test configurations and a subset of 3D scenes
- [DOM 3D Objects](https://gateway.infinitescript.com/?f=DOM-3D-Objects) – assets for data generation and benchmarking
- [DOM 3D Scenes](https://gateway.infinitescript.com/?f=DOM-3D-Scenes) – full scene assets for data generation
### Pretrained Models
- [DynamicVLA (trained on DOM)](https://huggingface.co/hzxie/dynamic-vla-DOM)
## Installation 📥
We recommend using **conda** to create two separate environments:
- one for **model training & inference**
- one for **Isaac Lab simulation & evaluation**
### PyTorch Environment
- Install **Python 3.10** and **PyTorch 2.7.1** *(Other versions should work, but are not fully tested)*
- Install dependencies:
```bash
pip install -r requirements.txt
```
### Isaac Lab Environment
- Install **Python 3.11** *(Other versions should work, but are not fully tested)*
- Install **Isaac Sim 5.1.0** and **Isaac Lab 2.3.2**
Follow the official guide: https://isaac-sim.github.io/IsaacLab/v2.3.2/source/setup/installation/index.html
- Install additional dependencies:
```bash
pip install shapely pyzmq h5py
```
## Benchmarking Your Policy 🏅
### Prepare scenes and objects
Download [DOM Testing Set](#dom-dataset) (including a subset of DOM 3D scenes) and [DOM 3D Objects](#dom-dataset).
```
PROJECT_ROOT/
├── objects/ # Put DOM 3D Objects here
├── scenes/ # Put DOM 3D Scenes here
| └── textures # Put the textures of 3D scenes here
| └── *.usd # Put the USD files of 3D scenes here
├── tests/ # Put DOM Testing Set here
| └── *.json
|── test-envs.txt # The list of test environments (included in DOM Testing Set)
├── datasets/ # Generated simulated datasets will be stored here
└── dynamic-vla/ # git clone https://github.com/hzxie/DynamicVLA dynamic-vla
└── runs # Create folder for evaluation and checkpoints output
```
### Run Policy Evaluation Server
> ⚠️ This step requires the **Isaac Lab environment**
From the `PROJECT_ROOT/dynamic-vla` directory, run:
```bash
python3 simulations/evaluate.py \
--scene_dir ../scenes \
--output_dir ../output/evaluation \
--env_cfg ../test-envs.txt \
--enable_cameras --headless -n 20 --save
```
**Arguments:**
- `test-envs.txt` are provided by [DOM Testing Set](#dom-dataset)
- `-n 20`: run 20 trials per environment
- `--save`: save evaluation videos to `output_dir`
- `--headless`: run without GUI
- `--enable_cameras`: enable visual observations
### Run Policy Inference
> ⚠️ This step requires the **PyTorch environment**
From the `PROJECT_ROOT/dynamic-vla` directory, run:
```bash
python3 scripts/inference.py \
-p /path/to/vla-checkpoint \
-r euler -d -s
```
**Arguments:**
- `-p`: path to the trained model checkpoint
- `-r euler`: use Euler angles for rotation representation
- `-d`: enable **delta actions** *(actions are relative to current state)*
- `-s`: enable **contiguous inference** *(if supported by the model)*
## Simulated Dataset Generation 🧪
> ⚠️ This step requires the **Isaac Lab environment**
### IsaacSim Simulation
From the `PROJECT_ROOT/dynamic-vla` directory, you can generate synthetic data using:
```bash
python3 simulations/simulate.py \
--headless --enable_cameras --seed 42 --save --task place
```
Example configuration file: `simulations/configs/sim_cfg.yaml`
**Arguments:**
- `--task` : task type to simulate. Options: `pick`, `place`, `long-horizon`.
- `--robot`: robot type used in simulation _(default: `franka`, also supports `piper`)_.
- `--headless`: run simulation without GUI.
- `--enable_cameras`: include visual observations in the output dataset.
- `--debug`: enable debug mode and render trajectories as `.mp4` videos.
- `--seed`: random seed for simulation _(automatically increments for each run if specified)_.
- `-n`, `--n_simulations`: number of simulation episodes to generate *(default: `10,000`)*.
- `--save`: save generated simulation data in HDF5 format.
### Trajectory Replay
After data generation, convert the trajectories into a format compatible with VLA training:
```bash
python3 scripts/translate_dataset_seq.py \
--dataset_dir ../datasets --output_dir ../datasets-tr \
--enable_cameras --headless --save
```
**Arguments:**
- `--dataset_dir`: directory containing the raw simulation datasets.
- `--output_dir`: directory to store the processed trajectories.
- `--enable_cameras`: include visual observations in the output dataset.
- `--headless`: run simulation without GUI.
- `--save`: save generated simulation data in HDF5 format.
### Convert LeRobot Dataset
We provide a script to convert the generated `.h5` files into the LeRobot dataset *(v2.1 format)*, using **Euler angles** as the rotation representation:
```bash
python3 scripts/create_lerobot_dataset.py \
--dataset_dir ../datasets-tr --repo hzxie/DOM --rotation euler
```
This will create lerobot dataset using all the hdf5 datasets in the default output directory.
## Training 👩🏽💻
> ⚠️ This step requires the **PyTorch environment**
From the `PROJECT_ROOT/dynamic-vla` directory, run:
```bash
torchrun --nnodes=1 --nproc_per_node=8 --standalone run.py \
-c configs/dynamicvla.yaml \
-p /path/to/pretrained/model
-d hzxie/DOM
```
**Arguments:**
- `--nnodes`: number of compute nodes (machines) used for distributed training
- `--nproc_per_node`: number of GPUs per node
- `-c`: path to the training config file
- `-p`: path to the pretrained model checkpoint *(optional)*
- `-d`: name of the LeRobot dataset *(v2.1 format)*
### Checkpoint Evaluation
> ⚠️ This step requires the **PyTorch environment**
During training, you can automatically evaluate checkpoints using the following script:
```bash
python3 scripts/eval_checkpoints.py \
-r euler -d -s -p "*fvit*46k*" \
--ckpt_dir ./runs/checkpoints/ \
```
**Arguments:**
- `--ckpt_dir`: directory containing the checkpoints to be evaluated.
- `-p`: pattern used to match checkpoint filenames *(supports wildcard patterns)*.
- `--host`: host address of the evaluation server *(default: `127.0.0.1`)*.
- `--img_port`: port used for the image stream on the evaluation server *(default: `3186`)*.
- `--act_port`: port used for the action stream on the evaluation server *(default: `3188`)*.
## License 🗒️
This project is licensed under [NTU S-Lab License 1.0](https://github.com/hzxie/DynamicVLA/blob/master/LICENSE). Redistribution and use should follow this license.
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