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InternRobotics
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Model Details
Model Provider
InternRobotics
Model Tree
Qwen/Qwen3-VL-30B-A3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
VLAC-Cut is a process-level multimodal trajectory critic for robot post-training data curation. Given a natural-language task instruction, an optional task plan, and a robot rollout video, VLAC-Cut estimates signed task progress over time and identifies temporal segments associated with task advancement or degradation.
Unlike methods that assume task progress increases monotonically over time, VLAC-Cut models non-monotonic execution dynamics, including advancement, stagnation, regression, and recovery. This formulation supports process-level analysis of partial completion, temporary failure, subsequent recovery, and rollout segmentation for post-training data selection.
This Hugging Face repository contains the VLAC-Cut model weights and loading assets. The official inference examples and evaluation code are maintained in the GitHub repository.
| Property | Description |
|---|---|
| Base model | Qwen/Qwen3-VL-30B-A3B-Instruct |
| Input | Task instruction, optional task plan, and sampled video frames |
| Output | Timestamped task-progress estimates |
| Sampling rate | 2 Hz-20 Hz |
| Default sampling rate | 2.0 Hz |
python
from transformers import AutoModelForImageTextToText, AutoProcessormodel_id = "InternRobotics/VLAC-Cut"processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained(model_id,dtype="auto",device_map="auto",)model.eval()
Run progress inference on a local video using the GitHub code:
bash
git clone https://github.com/InternRobotics/VLAC-cutcd VLAC-cutpython scripts/run_example.py \--model-path InternRobotics/VLAC-Cut \--video-path <path-to-video.mp4> \--task-instruction "<natural-language task instruction>" \--task-plan $'<optional step-by-step task plan>' \--output-jsonl <path-to-output.jsonl>
Render a prediction JSONL file as an annotated video:
bash
python scripts/utils/render_prediction_video.py \--input-jsonl <path-to-output.jsonl> \--output-video <path-to-preview.mp4>
Please cite the following paper when using VLAC-Cut, the released model, or the Video Progress Benchmark:
bibtex
@misc{zhai2026helphumanefficientlargescalerobot,title={HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation},author={Shaopeng Zhai and Qi Zhang and Tianyi Zhang and Haoran Zhang and Fuxian Huang and Zhanhui Lin and Zijun Xu and Weinan Zhang},year={2026},eprint={2607.09776},archivePrefix={arXiv},primaryClass={cs.RO},url={https://arxiv.org/abs/2607.09776},}
The model weights and third-party training data may be subject to additional licenses or terms of use. The source code in the GitHub repository is released under the MIT License.