OpenVLA: An Open-Source Vision-Language-Action Model

Getting Started | Pretrained VLAs | Installation | Fine-Tuning OpenVLA via LoRA | Fully Fine-Tuning OpenVLA | Training VLAs from Scratch | Evaluating OpenVLA | Project Website

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Latest Updates

• [2024-10-15] Added a VLA Performance Troubleshooting section to the README with best practices for debugging poor VLA performance after fine-tuning.
• [2024-09-04] Added LIBERO simulation benchmark fine-tuning experiments to paper (see v2 on arXiv); added instructions for reproducing OpenVLA results in LIBERO Simulation Benchmark Evaluations section
• [2024-08-14] Added new section, Evaluating OpenVLA, with instructions for running BridgeData V2 WidowX robot evals
• [2024-07-08] Added new sections: Fine-Tuning OpenVLA via LoRA, Fully Fine-Tuning OpenVLA
• [2024-06-13] Initial release

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A simple and scalable codebase for training and fine-tuning vision-language-action models (VLAs) for generalist robotic manipulation:

• Different Dataset Mixtures: We natively support arbitrary datasets in RLDS format, including arbitrary mixtures of data from the Open X-Embodiment Dataset.
• Easy Scaling: Powered by PyTorch FSDP and Flash-Attention, we can quickly and efficiently train models from 1B - 34B parameters, with easily adaptable model architectures.
• Native Fine-Tuning Support: Built-in support (with examples) for various forms of fine-tuning (full, partial, LoRA).

Built on top of Prismatic VLMs.

Getting Started

To get started with loading and running OpenVLA models for inference, we provide a lightweight interface that leverages HuggingFace transformers AutoClasses, with minimal dependencies.

For example, to load openvla-7b for zero-shot instruction following in the BridgeData V2 environments with a WidowX robot:

# Install minimal dependencies (`torch`, `transformers`, `timm`, `tokenizers`, ...)
# > pip install -r https://raw.githubusercontent.com/openvla/openvla/main/requirements-min.txt
from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image

import torch

# Load Processor & VLA
processor = AutoProcessor.from_pretrained("openvla/openvla-7b", trust_remote_code=True)
vla = AutoModelForVision2Seq.from_pretrained(
    "openvla/openvla-7b", 
    attn_implementation="flash_attention_2",  # [Optional] Requires `flash_attn`
    torch_dtype=torch.bfloat16, 
    low_cpu_mem_usage=True, 
    trust_remote_code=True
).to("cuda:0")

# Grab image input & format prompt
image: Image.Image = get_from_camera(...)
prompt = "In: What action should the robot take to {<INSTRUCTION>}?\nOut:"

# Predict Action (7-DoF; un-normalize for BridgeData V2)
inputs = processor(prompt, image).to("cuda:0", dtype=torch.bfloat16)
action = vla.predict_action(**inputs, unnorm_key="bridge_orig", do_sample=False)

# Execute...
robot.act(action, ...)

We also provide an example script for fine-tuning OpenVLA models for new tasks and embodiments; this script supports different fine-tuning modes -- including (quantized) low-rank adaptation (LoRA) supported by HuggingFace's PEFT library.

For deployment, we provide a lightweight script for serving OpenVLA models over a REST API, providing an easy way to integrate OpenVLA models into existing robot control stacks, removing any requirement for powerful on-device compute.

Pretrained VLAs

We release two OpenVLA models trained as part of our work, with checkpoints, configs, and model cards available on our HuggingFace page:

• openvla-7b: The flagship model from our paper, trained from the Prismatic prism-dinosiglip-224px VLM (based on a fused DINOv2 and SigLIP vision backbone, and Llama-2 LLM). Trained on a large mixture of datasets from Open X-Embodiment spanning 970K trajectories (mixture details - see "Open-X Magic Soup++").
• openvla-v01-7b: An early model used during development, trained from the Prismatic siglip-224px VLM (singular SigLIP vision backbone, and a Vicuña v1.5 LLM). Trained on the same mixture of datasets as Octo, but for significantly fewer GPU hours than our final model (mixture details - see "Open-X Magic Soup").

Explicit Notes on Model Licensing & Commercial Use: While all code in this repository is released under an MIT License, our pretrained models may inherit restrictions from the underlying base models we use. Specifically, both the above models are derived from Llama-2, and as such are subject to the Llama Community License.

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Installation

Note: These installation instructions are for full-scale pretraining (and distributed fine-tuning); if looking to just run inference with OpenVLA models (or perform lightweight fine-tuning), see instructions above!

This repository was built using Python 3.10, but should be backwards compatible with any Python >= 3.8. We require PyTorch 2.2.* -- installation instructions can be found here. The latest version of this repository was developed and thoroughly tested with:

• PyTorch 2.2.0, torchvision 0.17.0, transformers 4.40.1, tokenizers 0.19.1, timm 0.9.10, and flash-attn 2.5.5

[5/21/24] Note: Following reported regressions and breaking changes in later versions of transformers, timm, and tokenizers we explicitly pin the above versions of the dependencies. We are working on implementing thorough tests, and plan on relaxing these constraints as soon as we can.

Use the setup commands below to get started:

# Create and activate conda environment
conda create -n openvla python=3.10 -y
conda activate openvla

# Install PyTorch. Below is a sample command to do this, but you should check the following link
# to find installation instructions that are specific to your compute platform:
# https://pytorch.org/get-started/locally/
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia -y  # UPDATE ME!

# Clone and install the openvla repo
git clone https://github.com/openvla/openvla.git
cd openvla
pip install -e .

# Install Flash Attention 2 for training (https://github.com/Dao-AILab/flash-attention)
#   =>> If you run into difficulty, try `pip cache remove flash_attn` first
pip install packaging ninja
ninja --version; echo $?  # Verify Ninja --> should return exit code "0"
pip install "flash-attn==2.5.5" --no-build-isolation

If you run into any problems during the installation process, please file a GitHub Issue.

Note: See vla-scripts/ for full training and verification scripts for OpenVLA models. Note that scripts/ is mostly a holdover from the original (base) prismatic-vlms repository, with support for training and evaluating visually-conditioned language models; while you can use this repo to train VLMs AND VLAs, note that trying to generate language (via scripts/generate.py) with existing OpenVLA models will not work (as we only train current OpenVLA models to generate actions, and actions alone).

Fine-Tuning OpenVLA via LoRA

In this section, we discuss fine-tuning OpenVLA using Low-Rank Adaptation (LoRA) via the Hugging Face transformers library, which is recommended if you do not have sufficient compute to fully fine-tune a 7B-parameter model. The main script for LoRA fine-tuning is vla-scripts/finetune.py. (If you instead wish to do full fine-tuning, please see the Fully Fine-Tuning OpenVLA section.)

Below we show an example of how you can fine-tune the main OpenVLA checkpoint (openvla-7b) via LoRA. Here we fine-tune on BridgeData V2 using a single A100 GPU with 80 GB VRAM. (You can also fine-tune with a smaller GPU, as long as it has at least ~27 GB of memory, by modifying the batch size.)

First, download the BridgeData V2 dataset:

# Change directory to your base datasets folder
cd <PATH TO BASE DATASETS DIR>

# Download the full dataset (124 GB)
wget -r -nH --cut-dirs=4 --reject="index.html*" https://rail.eecs.berkeley.edu/datasets/bridge_release/data/tfds/bridge_dataset/

# Rename the dataset to `bridge_orig` (NOTE: Omitting this step may lead to runtime errors later)
mv bridge_dataset bridge_orig

Now, launch the LoRA fine-tuning script, as shown below. Note that --batch_size==16 with --grad_accumulation_steps==1 requires ~72 GB GPU memory. If you have a smaller GPU, you should reduce --batch_size and increase --grad_accumulation_steps to maintain an effective batch size that is large enough for stable training. If you have multiple GPUs and wish to train via PyTorch Distributed Data Parallel (DDP), simply set --nproc-per-node in the torchrun command below to the number of available GPUs.

torchrun --standalone --nnodes 1 --nproc-per-node 1 vla-scripts/finetune.py \
  --vla_path "openvla/openvla-7b" \
  --data_root_dir <PATH TO BASE DATASETS DIR> \
  --dataset_name bridge_orig \
  --run_root_dir <PATH TO LOG/CHECKPOINT DIR> \
  --adapter_tmp_dir <PATH TO TEMPORARY DIR TO SAVE ADAPTER WEIGHTS> \
  --lora_rank 32 \
  --batch_size 16 \
  --grad_accumulation_steps 1 \
  --learning_rate 5e-4 \
  --image_aug <True or False> \
  --wandb_project <PROJECT> \
  --wandb_entity <ENTITY> \
  --save_steps <NUMBER OF GRADIENT STEPS PER CHECKPOINT SAVE>

Note: If you set --image_aug==False in the command above, you will observe nearly 100% action_accuracy in the training logs, since the openvla-7b model is already pretrained (without augmentations) on a superset of datasets that includes BridgeData V2.

To LoRA fine-tune on a different dataset, you can download the dataset from the Open X-Embodiment (OXE) mixture (see this custom script for an example of how to download datasets from OXE). Alternatively, if you have a custom dataset that is not part of OXE, you can either (a) convert the dataset to the RLDS format which is compatible with our fine-tuning script (see this repo for instructions on this), or (b) use your own custom PyTorch Dataset wrapper (see comments in vla-scripts/finetune.py for instructions). We recommend option (a) for most users; the RLDS dataset and dataloader are tested more extensively since we used these for all of our pretraining and fine-tuning experiments.

For option (a), after you converted your dataset to RLDS, you need to register it with our data loader, by registering a dataset config here and a dataset transform function here.

Once you have integrated your new dataset, you can launch LoRA fine-tuning with the same vla-scripts/finetune.py script above. If you run into any issues, please visit the VLA Troubleshooting section or search for a similar issue in the OpenVLA GitHub Issues page (including "Closed" issues). If you cannot find a similar issue there, feel free to create a new issue.

Fully Fine-Tuning OpenVLA

In this section, we discuss fully fine-tuning OpenVLA (all 7.5 billion parameters) via native PyTorch Fully Sharded Data Parallel (FSDP) using the Prismatic VLMs training script. Full fine-tuning is more advanced/involved and is only recommended if you have sufficient compute (e.g., a full node of 8 A100 GPUs) and if LoRA fine-tuning is insufficient for your use case (e.g., if the fine-tuning distribution varies drastically from the pretraining distribution). Otherwise, we recommend that you try parameter-efficient fine-tuning via LoRA, which is described in the Fine-Tuning OpenVLA via LoRA section.

For full fine-tuning, you will need to download a different version of the OpenVLA model checkpoint that is compatible with the Prismatic VLMs codebase, which we built on top of to develop the OpenVLA model. You can download this Prismatic-compatible OpenVLA checkpoint using the git commands below (alternatively, you can download via the Hugging Face CLI):

# Change directory to your base model checkpoints folder
cd <PATH TO BASE MODEL CHECKPOINTS DIR>

# Download checkpoint (30 GB) -- may take a few minutes
git clone [email protected]:openvla/openvla-7b-prismatic

# If the command above did not download the full checkpoint,
# manually fetch it via git Large File Storage (LFS)
# Note: You may have to configure an SSH key for this to work
cd openvla-7b-prismatic
git lfs fetch --all

We show how you can fully fine-tune OpenVLA on BridgeData V2 using a single node with 8 GPUs. If you wish to use a different number of GPUs (or nodes), you can modify the VLA training configuration in prismatic/conf/vla.py.

Download the BridgeData V2 dataset:

# Change directory to your base datasets folder
cd <PATH TO BASE DATASETS DIR>

# Download the full dataset (124 GB)
wget -r -nH --cut-dirs=4 --reject="index.html*" https://rail.eecs.berkeley.edu/datasets/bridge_release/data/tfds/bridge_dataset/

# Rename the dataset to `bridge_orig` (NOTE: Omitting this step may lead to runtime errors later)
mv bridge_dataset bridge_orig

Next, create a Hugging Face user access token and copy the token value (a string that starts with hf_...) into a file named .hf_token at the root directory of this repo (openvla/.hf_token).

# Go to openvla root directory
cd openvla

# Copy HF token value into token file. Replace "hf_..." with your own token value!
# See: https://huggingface.co/docs/hub/en/security-tokens
echo hf_... >>> .hf_token

Now, launch the training script. If you wish to use a different number of nodes or GPUs, modify the VLA training configuration in prismatic/conf/vla.py and then change the --nnodes and --nproc-per-node arguments below accordingly.

torchrun --standalone --nnodes 1 --nproc-per-node 8 vla-scripts/train.py \
  --pretrained_checkpoint <PATH TO openvla/openvla-7b-prismatic CHECKPOINT FILE: step-295000-epoch-40-loss=0.2200.pt> \
  --vla.type prism-dinosiglip-224px+mx-bridge \
  --data_root_dir <PATH TO BASE DATASETS DIR> \
  --run_root_dir <PATH TO LOG/CHECKPOINT DIR> \
  --run_id <OPTIONAL RUN ID FOR WANDB LOGGING> \
  --image_aug <True or False> \
  --wandb_project <PROJECT> \
  --wandb_entity <ENTITY> \
  --save_interval <NUMBER OF GRADIENT STEPS PER CHECKPOINT SAVE> \
  --is_resume False

Note that the --is_resume argument is set to False above since we are fine-tuning a pretrained checkpoint rather than resuming a paused training run.

If your training run gets paused and you wish to resume from the latest checkpoint, change --pretrained_checkpoint to the latest checkpoint path, and then set --is_resume==True and specify --resume_step and --resume_epoch as the step and epoch number, respectively. For example, if you wish to resume training from a checkpoint named step-010000-epoch-20-loss=0.0160.pt, you would set is_resume==True, resume_step==10000, and resume_epoch==20.

Note: If you run the BridgeData V2 fine-tuning command above, you should observe nearly 100% Action Token Accuracy in the training logs, since the openvla-7b model is already pretrained on a superset of datasets that includes BridgeData V2.

To fully fine-tune OpenVLA on a different dataset, you can download the dataset from the Open X-Embodiment (OXE) mixture (see this custom script for an example of how to download datasets from OXE). Alternatively, if you have a custom dataset that is not part of OXE, you can convert the dataset to the RLDS format, which is compatible with our fine-tuning script (see this repo for instructions on this). After downloading/converting the dataset, you will need to modify the following files:

• prismatic/conf/vla.py: Add a new training configuration by creating an experiment class, and then register it in the VLARegistry at the bottom of the file.
  • Make sure to create a new unique vla_id for your fine-tuning run, and adjust some configuration variables as needed – e.g., expected_world_size (number of GPUs), per_device_batch_size (batch size per GPU), global_batch_size (total batch size), shuffle_buffer_size (number of samples in shuffle buffer per GPU), etc. See comments under the VLAConfig class at the top of the file to understand the purpose of each variable.
• prismatic/vla/datasets/rlds/oxe/mixtures.py: Define a new mixture for your fine-tuning mixture in the OXE_NAMED_MIXTURES dictionary.
• prismatic/vla/datasets/rlds/oxe/transforms.py: Define a new dataset transform function for your fine-tuning dataset, and add it to the OXE_STANDARDIZATION_TRANSFORMS registry at the bottom of the file.
• prismatic/vla/datasets/rlds/oxe/configs.py: Add a new configuration specifying your fine-tuning dataset's observation and action spaces to the OXE_DATASET_CONFIGS dictionary.

After completing the steps above, you can start full fine-tuning using the vla-scripts/train.py script. Make sure to set the --vla.type argument to the new vla_id that you added in prismatic/conf/vla.py.

When you are finished with fine-tuning, you will need to convert the final model checkpoint to a version that is compatible with the Hugging Face transformers library. See the Converting Prismatic Models to Hugging Face section for instructions.

If you run into any issues, please visit the VLA Troubleshooting section or search for a similar issue in the OpenVLA GitHub Issues page (including "Closed" issues). If you cannot find a similar issue there, feel free to create a new issue.

Converting Prismatic Models to Hugging Face

If you have used the Prismatic VLMs codebase to train your model (e.g., if you did full fine-tuning of OpenVLA on a new dataset), you will need to convert the final checkpoint to a version that is compatible with Hugging Face transformers AutoClasses. We discuss how to do so in this section.

Let's say your training run directory is PRISMATIC_RUN_DIR (e.g., prism-dinosiglip-224px+mx-oxe-magic-soup-plus+n8+b32+x7). Inside this directory, there should be a directory called checkpoints which contains saved model checkpoints (e.g., step-295000-epoch-40-loss=0.2200.pt). The Prismatic-to-Hugging-Face conversion script (convert_openvla_weights_to_hf.py) expects a checkpoint file named latest-checkpoint.pt. Therefore, you should first create a symbolic link called latest-checkpoint.pt that points to the checkpoint file that you wish to convert:

# Go to your Prismatic training run's `checkpoints` directory
cd PRISMATIC_RUN_DIR/checkpoints

# Create symbolic link pointing to your checkpoint file
ln -s <YOUR CHECKPOINT FILENAME> latest-checkpoint.pt

Then, launch the conversion script to convert the checkpoint from the Prismatic VLMs format to the Hugging Face format:

python vla-scripts/extern/convert_openvla_weights_to_hf.py \
    --openvla_model_path_or_id <PRISMATIC_RUN_DIR> \
    --output_hf_model_local_path <OUTPUT DIR FOR CONVERTED CHECKPOINT>

The command above will save the HF-compatible checkpoint in output_hf_model_local_path. Now you can load the checkpoint with HF AutoClasses as normal, as shown below. Note that there is an additional necessary step to register the OpenVLA model to HF AutoClasses before loading it because you are loading a locally saved checkpoint rather than one that is pushed to the HF Hub (see here for details).

import torch
from transformers import AutoConfig, AutoImageProcessor, AutoModelForVision2Seq, AutoProcessor

from prismatic.extern.hf.configuration_prismatic import OpenVLAConfig
from prismatic.extern.hf.modeling_prismatic import OpenVLAForActionPrediction
from prismatic.extern.hf.processing_prismatic import PrismaticImageProcessor, PrismaticProcessor

# Register OpenVLA model to HF AutoClasses (not needed if you pushed model to HF Hub)
AutoConfig.register("openvla", OpenVLAConfig)
AutoImageProcessor.register(OpenVLAConfig, PrismaticImageProcessor)
AutoProcessor.register(OpenVLAConfig, PrismaticProcessor)
AutoModelForVision2Seq.register(OpenVLAConfig, OpenVLAForActionPrediction)

# Load Processor & VLA
processor = AutoProcessor.from_pretrained("<PATH TO CONVERTED CHECKPOINT DIR>", trust_remote_code=True)
vla = AutoModelForVision2Seq.from_pretrained(
    "<PATH TO CONVERTED CHECKPOINT DIR>",
    attn_implementation="flash_attention_2",  # [Optional] Requires `flash_attn`
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
).to("cuda:0")

...

Training VLAs from Scratch

We provide full instructions and configurations for training VLA models on (arbitrary subsets of) the Open X-Embodiment (OXE) Dataset. If you run in to any issues with the following, see VLA Troubleshooting below (or file a GitHub Issue).

VLA Pretraining Datasets

We download and preprocess individual datasets from Open X-Embodiment in RLDS format following this custom script. See mixtures.py for the full list of component datasets (and mixture weights) we use to train openvla-7b.

• Important: For the BridgeData V2 component, the version in OXE is out of date (as of 12/20/2023). Instead, you should download the dataset from the official website and place it under the subdirectory bridge_orig/. Replace any reference to bridge in the OXE code with bridge_orig.

VLA Configuration & Training Script

The entry point for VLA training is vla-scripts/train.py. We use draccus to provide a modular, dataclass-based interface for specifying VLA training configurations; existing VLA configurations are in prismatic/conf/vla.py. You can add your own training configuration and refer to it using the --vla.type command line argument.

We use PyTorch Fully Sharded Data Parallel (FSDP) to distribute training across GPUs. Launch training via torchrun:

# Train VLA on BridgeData V2 with the Prismatic DINO-SigLIP 224px Backbone on a Single Node (w/ 8 GPUs)
torchrun --standalone --nnodes 1 --nproc-per-node 8 vla-scripts/train.py \
  --vla.type "prism-dinosiglip-224px+mx-bridge" \
  --data_root_dir <PATH TO OXE DATA ROOT> \
  --run_root_dir <PATH TO LOG/CHECKPOINT ROOT> \
  --wandb_project "<PROJECT>" \
  --wandb_entity "<ENTITY>"

VLA Troubleshooting

The following are a list of known problems and corresponding fixes:

FileNotFoundError: Failed to construct dataset "fractal20220817_data", builder_kwargs "{'data_dir': '/path/to/processed/datasets/'}": Could not load dataset info from fractal20220817_data/0.1.0/dataset_info.json

• Fix: Downgrade tensorflow-datasets via pip install tensorflow-datasets==4.9.3.

AttributeError: 'DLataset' object has no attribute 'traj_map'. Did you mean: 'flat_map'?

• Fix: Upgrade dlimp to the newest version. You may have to --force-reinstall like so: pip install --no-deps --force-reinstall git+https://github.com/moojink/dlimp_openvla

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Evaluating OpenVLA

BridgeData V2 WidowX Evaluations

Setup

Clone the BridgeData V2 WidowX controller repo and install the widowx_envs package:

git clone https://github.com/rail-berkeley/bridge_data_robot.git
cd bridge_data_robot
pip install -e widowx_envs

Additionally, install the edgeml library:

git clone https://github.com/youliangtan/edgeml.git
cd edgeml
pip install -e .

Follow the instructions in the bridge_data_robot README to create the Bridge WidowX Docker container.

Launching BridgeData V2 Evaluations

There are multiple ways to run BridgeData V2 evaluations. We describe the server-client method below.

In one Terminal window (e.g., in tmux), start the WidowX Docker container:

cd bridge_data_robot
./generate_usb_config.sh
USB_CONNECTOR_CHART=$(pwd)/usb_connector_chart.yml docker compose up --build robonet

In a second Terminal window, run the WidowX robot server:

cd bridge_data_robot
docker compose exec robonet bash -lic "widowx_env_service --server"

In a third Terminal window, run the OpenVLA policy evaluation script:

cd openvla
python experiments/robot/bridge/run_bridgev2_eval.py \
  --model_family openvla \
  --pretrained_checkpoint openvla/openvla-7b

If you run into any problems with evaluations, please file a GitHub Issue.

LIBERO Simulation Benchmark Evaluations

In the updated OpenVLA paper (v2), we discuss fine-tuning OpenVLA on a simulated benchmark, LIBERO, in Appendix E. Please see the paper for details, such as how we modify the provided demonstration datasets to improve the overall performance of all methods.

We copy the results to the section below and then discuss how to reproduce the results for OpenVLA.

OpenVLA Fine-Tuning Results

Method	LIBERO-Spatial	LIBERO-Object	LIBERO-Goal	LIBERO-Long	Average
Diffusion Policy from scratch	78.3 ± 1.1%	92.5 ± 0.7%	68.3 ± 1.2%	50.5 ± 1.3%	72.4 ± 0.7%
Octo fine-tuned	78.9 ± 1.0%	85.7 ± 0.9%	84.6 ± 0.9%	51.1 ± 1.3%	75.1 ± 0.6%
OpenVLA fine-tuned (ours)	84.7 ± 0.9%	88.4 ± 0.8%	79.2 ± 1.0%	53.7 ± 1.3%	76.5 ± 0.6%

Each success rate is the average over 3 random seeds x 500 rollouts each (10 tasks x 50 rollouts per task).

LIBERO Setup

Clone and install the LIBERO repo:

git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git
cd LIBERO
pip install -e .

Additionally, install other required packages:

cd openvla
pip install -r experiments/robot/libero/libero_requirements.txt

(Optional) To download the modified versions of the LIBERO datasets that we used in our fine-tuning experiments, run the command below. This will download the LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10 datasets in RLDS data format (~10 GB total). You can use these to fine-tune OpenVLA or train other methods. This step is optional since we provide pretrained OpenVLA checkpoints below. (Also, you can find the script we used to generate the modified datasets in raw HDF5 format here and the code we used to convert these datasets to the RLDS format here.)

git clone [email protected]:datasets/openvla/modified_libero_rlds

Launching LIBERO Evaluations

We fine-tuned OpenVLA via LoRA (r=32) on four LIBERO task suites independently: LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10 (also called LIBERO-Long). The four checkpoints are available on Hugging Face:

• openvla/openvla-7b-finetuned-libero-spatial
• openvla/openvla-7b-finetuned-libero-object
• openvla/openvla-7b-finetuned-libero-goal
• openvla/openvla-7b-finetuned-libero-10

To start evaluation with one of these checkpoints, run one of the commands below. Each will automatically download the appropriate checkpoint listed above.

# Launch LIBERO-Spatial evals
python experiments/robot/libero/run_libero_eval.py \
  --model_family openvla \
  --pretrained_checkpoint openvla/openvla-7b-finetuned-libero-spatial \
  --task_suite_name libero_spatial \
  --center_crop True

# Launch LIBERO-Object evals
python experiments/robot/libero/run_libero_eval.py \
  --model_family openvla \
  --pretrained_checkpoint openvla/openvla-7b-finetuned-libero-object \
  --task_suite_name libero_object \
  --center_crop True

# Launch LIBERO-Goal evals
python experiments/robot/libero/run_libero_eval.py \
  --model_family openvla \
  --pretrained_checkpoint openvla/openvla-7b-finetuned-libero-goal \
  --task_suite_name libero_goal \
  --center_crop True

# Launch LIBERO-10 (LIBERO-Long) evals
python experiments/robot/libero/run_libero_eval.py \
  --model_family openvla \
  --pretrained_checkpoint openvla/openvla-7b-finetuned-libero-10 \
  --task_suite_name libero_10 \
  --center_crop True

Notes:

• The evaluation script will run 500 trials by default (10 tasks x 50 episodes each). You can modify the number of trials per task by setting --num_trials_per_task. You can also change the random seed via --seed.
• NOTE: Setting --center_crop True is important because we fine-tuned OpenVLA with random crop augmentations (we took a random crop with 90% area in every training sample, so at test time we simply take the center 90% crop).
• The evaluation script logs results locally. You can also log results in Weights & Biases by setting --use_wandb True and specifying --wandb_project <PROJECT> and --wandb_entity <ENTITY>.
• The results reported in our paper were obtained using Python 3.10.13, PyTorch 2.2.0, transformers 4.40.1, and flash-attn 2.5.5 on an NVIDIA A100 GPU, averaged over three random seeds. Please stick to these package versions. Note that results may vary slightly if you use a different GPU for evaluation due to GPU nondeterminism in large models (though we have tested that results were consistent across different machines with A100 GPUs).

Please file a GitHub Issue if you run into any problems.

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Repository Structure

High-level overview of repository/project file-tree:

• prismatic - Package source; provides core utilities for model loading, training, data preprocessing, etc.
• vla-scripts/ - Core scripts for training, fine-tuning, and deploying VLAs.
• experiments/ - Code for evaluating OpenVLA policies in robot environments.
• LICENSE - All code is made available under the MIT License; happy hacking!
• Makefile - Top-level Makefile (by default, supports linting - checking & auto-fix); extend as needed.
• pyproject.toml - Full project configuration details (including dependencies), as well as tool configurations.
• README.md - You are here!

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VLA Performance Troubleshooting

In this section we cover best practices for debugging poor VLA performance after fine-tuning on your target domain robot dataset.

Note: OpenVLA typically requires fine-tuning on a small demonstration dataset (~100 demos) from your target domain robot. Out-of-the-box, it only works well on domains from the training dataset.

Sanity checks:

• replay the actions from a demonstration from your fine-tuning dataset and make sure that the robot can execute the task successfully (this ensures that your data collection pipeline is correct)
• once you fine-tuned a model, load the model in your inference pipeline (as if you would run it to control the robot), but feed images from the fine-tuning dataset into the model (pretending they come from the robot) and verify that you can reproduce the token accuracies / L1 errors from training (this ensures that your inference pipeline is correct)

Best practices for fine-tuning data collection: If your setup passed the above two sanity checks, the issue may not be in model training, but in the data you fine-tuned the model with. Some best practices for data collection:

• Collect at a control frequency around 5-10Hz. OpenVLA is not trained with action chunking, empirically the model struggles with high-frequency data. If your robot setup uses a high-frequency controller (eg 50 Hz), consider downsampling your actions to 5Hz. Verify first that your robot can still solve the task when using 5Hz actions (ie repeat sanity check (1) above with 5Hz actions)
• Avoid pauses / small actions during data collection. Because OpenVLA is trained without action chunking, the model can be sensitive to idle actions in the fine-tuning data. If your data contains steps in which the robot barely moves, the model may "get stuck" in these steps at inference time. Try to collect fine-tuning demonstrations with continuous, slow movement.
• Ensure sufficient data coverage. If you plan to test the model with some variation, e.g. different initial positions of objects, make sure that your fine-tuning data contains sufficient diversity of such conditions as well, e.g. shows demonstrations with diverse initial conditions.
• Use consistent task strategies during data collection. This is not a hard constraint, but may make your life easier. Try to demonstrate tasks in consistent ways, e.g. approach objects from the same side, perform sub-steps in the same order even if they could be performed in arbitrary sequences. Being consistent gives you a less multi-modal fine-tuning dataset, which makes the modeling problem easier.

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Citation

If you find our code or models useful in your work, please cite our paper:

@article{kim24openvla,
    title={OpenVLA: An Open-Source Vision-Language-Action Model},
    author={{Moo Jin} Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
    journal = {arXiv preprint arXiv:2406.09246},
    year={2024}
}