Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
This repository contains the OpenVLA-OFT checkpoint for LIBERO-Long (also called LIBERO-10), as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques.
Project Page: https://openvla-oft.github.io/
Code: https://github.com/openvla-oft/openvla-oft
See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?search_models=oft
Quick Start
This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.
import pickle
from experiments.robot.libero.run_libero_eval import GenerateConfig
from experiments.robot.openvla_utils import get_action_head, get_processor, get_proprio_projector, get_vla, get_vla_action
from prismatic.vla.constants import NUM_ACTIONS_CHUNK, PROPRIO_DIM
# Instantiate config (see class GenerateConfig in experiments/robot/libero/run_libero_eval.py for definitions)
cfg = GenerateConfig(
pretrained_checkpoint = "moojink/openvla-7b-oft-finetuned-libero-spatial",
use_l1_regression = True,
use_diffusion = False,
use_film = False,
num_images_in_input = 2,
use_proprio = True,
load_in_8bit = False,
load_in_4bit = False,
center_crop = True,
num_open_loop_steps = NUM_ACTIONS_CHUNK,
unnorm_key = "libero_spatial_no_noops",
)
# Load OpenVLA-OFT policy and inputs processor
vla = get_vla(cfg)
processor = get_processor(cfg)
# Load MLP action head to generate continuous actions (via L1 regression)
action_head = get_action_head(cfg, llm_dim=vla.llm_dim)
# Load proprio projector to map proprio to language embedding space
proprio_projector = get_proprio_projector(cfg, llm_dim=vla.llm_dim, proprio_dim=PROPRIO_DIM)
# Load sample observation:
# observation (dict): {
# "full_image": primary third-person image,
# "wrist_image": wrist-mounted camera image,
# "state": robot proprioceptive state,
# "task_description": task description,
# }
with open("experiments/robot/libero/sample_libero_spatial_observation.pkl", "rb") as file:
observation = pickle.load(file)
# Generate robot action chunk (sequence of future actions)
actions = get_vla_action(cfg, vla, processor, observation, observation["task_description"], action_head, proprio_projector)
print("Generated action chunk:")
for act in actions:
print(act)
Citation
@article{kim2025fine,
title={Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success},
author={Kim, Moo Jin and Finn, Chelsea and Liang, Percy},
journal={arXiv preprint arXiv:2502.19645},
year={2025}
}
