CLAP-Qwen3.5-2B

CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding checkpoint: Qwen/Qwen3.5-2B fully fine-tuned with the CLAP recipe on LIBERO benchmark tasks.

CLAP (Causal Language-Action Prediction) converts a pretrained VLM into a VLA with no architectural changes: it prepends each numeric action-token sequence with a natural-language action description (a "language-action plan"), causally conditioning precise action-token prediction on that plan while staying close to the VLM's pretrained language distribution.

Quick Start

1. Download

pip install huggingface_hub
hf download omron-sinicx/CLAP-Qwen3.5-2B --local-dir ./CLAP-Qwen3.5-2B

2. Load with transformers

import pickle
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLProcessor

ckpt_dir = "./CLAP-Qwen3.5-2B"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    f"{ckpt_dir}/model_final", torch_dtype=torch.bfloat16, device_map="auto",
)
processor = Qwen2_5_VLProcessor.from_pretrained(f"{ckpt_dir}/model_final")

# Load dataset stats (required for action denormalization)
with open(f"{ckpt_dir}/dataset_stats.pkl", "rb") as f:
    dataset_stats = pickle.load(f)

3. Load with the CLAP training framework

from rv_train.train import get_pretrained_model

model, cfg = get_pretrained_model("./CLAP-Qwen3.5-2B", device=0)
model.eval()

`dataset_stats.pkl`

Action normalization statistics computed from the training dataset. Required at inference time to denormalize model outputs back to the original action space.

import pickle

with open("dataset_stats.pkl", "rb") as f:
    stats = pickle.load(f)
# stats contains mean/std for action dimensions

Checkpoint

This is the main branch weights, corresponding to training step 18000.

Training Details

Base Model: Qwen/Qwen3.5-2B
Method: CLAP (Causal Language-Action Prediction), Full Fine-Tuning
Prefix masking augmentation: disabled (action_mask_aug_per: 0.0) — "without prefix masking" variant
Dataset: LIBERO (via RoboVerse)
Framework: CLAP LIBERO Training

Files

File	Description
`model_final/model.safetensors`	Full model weights
`model_final/config.json`	Model configuration
`model_final/tokenizer.json`	Tokenizer
`dataset_stats.pkl`	Action normalization statistics (required for inference)
`config.yaml`	Training configuration

Citation

@inproceedings{ishitoya2026clap,
  title={CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding},
  author={Yuri Ishitoya and Jeremy Siburian and Masashi Hamaya and Haruto Suzuki and Kuniaki Saito and Toshihiko Fukushima and Cristian C. Beltran-Hernandez and Mai Nishimura},
  booktitle={10th Annual Conference on Robot Learning},
  year={2026}
}