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X-VLA (LeRobot)

X-VLA is a Vision-Language-Action foundation model that uses soft prompts to handle cross-embodiment and cross-domain robot control within a unified Transformer architecture.

Checkpoint fine-tuned on BridgeData for pick-and-place experiments on compact WidowX platforms. Demonstrates robust manipulation capabilities.

Original paper: X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model Reference implementation: https://github.com/2toinf/X-VLA
LeRobot implementation: Follows the original reference code for compatibility.

Model description

• Inputs: images (multi-view), proprio/state, optional language instruction
• Outputs: continuous actions
• Training objective: flow matching
• Action representation: continuous
• Intended use: Base model to fine tune on your specific use case

Quick start (inference on a real batch)

Installation

pip install "lerobot[xvla]"

For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation

Load model + dataset, run select_action

import torch
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.policies.factory import make_pre_post_processors

# Swap this import per-policy
from lerobot.policies.xvla.modeling_xvla import XVLAPolicy

# load a policy
model_id = "lerobot/xvla-widowx"  # <- swap checkpoint
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

policy = XVLAPolicy.from_pretrained(model_id).to(device).eval()

preprocess, postprocess = make_pre_post_processors(
    policy.config,
    model_id,
    preprocessor_overrides={"device_processor": {"device": str(device)}},
)
# load a lerobotdataset (we will replace with a simpler dataset)
dataset = LeRobotDataset("lerobot/libero")

# pick an episode
episode_index

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