| --- |
| license: other |
| license_name: slab-license |
| license_link: LICENSE |
| datasets: |
| - hzxie/DOM |
| base_model: |
| - HuggingFaceTB/SmolLM2-360M |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - lerobot |
| - dynamicvla |
| --- |
| |
|
|
|
|
| # Model Card for DynamicVLA |
|
|
| [DynamicVLA](https://arxiv.org/abs/2601.22153) is a vision-language-action model for dynamic object manipulation. |
| It is designed to handle dynamic scenes that require fast perception, temporal anticipation, and continuous control. |
|
|
| This model is trained and evaluated using the official [DynamicVLA codebase](https://github.com/hzxie/DynamicVLA). |
| For full setup, training, and benchmarking instructions, please refer to the repository README. |
|
|
| * * * |
|
|
| ## How to Get Started with the Model |
|
|
| For a complete walkthrough, see the official DynamicVLA repository. Below is the short version for training and running inference/evaluation. |
|
|
| ### Train from scratch |
|
|
| From the `PROJECT_ROOT/dynamic-vla` directory, run: |
|
|
| ```bash |
| torchrun --nnodes=1 --nproc_per_node=8 --standalone run.py \ |
| -c configs/dynamicvla.yaml \ |
| -d hzxie/DOM |
| ``` |
|
|
| ### Evaluate the policy / run inference |
|
|
| ```bash |
| # 1. start evaluation server |
| python3 simulations/evaluate.py \ |
| --scene_dir ../scenes \ |
| --output_dir ../output/evaluation \ |
| --env_cfg ../test-envs.txt \ |
| --enable_cameras --headless -n 20 --save |
| |
| # 2. run policy inference |
| python3 scripts/inference.py \ |
| -p /path/to/vla-checkpoint \ |
| -r euler -d -s |
| ``` |
|
|
