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zaleni/
MagicBot-VGA-Robotwin

Name: MagicBot-VGA-Robotwin
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MagicBot-VGA

This repository documents how to evaluate our RoboTwin model zaleni/MagicBot-VGA-Robotwin with the MagicBot-VGA codebase.

Repository

Model

This README focuses on RoboTwin 2.0 environment preparation and evaluation.

It covers:

MagicBot environment installation
RoboTwin evaluation setup
required external model assets
single-task evaluation
50-task randomized evaluation
CVPR 2026 RoboTwin Track 11-task evaluation
submission package generation for the leaderboard workflow

1. Requirements

The codebase is built and tested with:

Python 3.10
CUDA 12.8
PyTorch 2.7.1

We recommend using a Linux machine with NVIDIA GPUs.

2. Install the MagicBot Base Environment

Clone the repository:

bash

git clone https://github.com/zaleni/MagicBot-VGA.git
cd MagicBot-VGA

Create a conda environment:

bash

conda create -y -n magicbot python=3.10
conda activate magicbot
pip install --upgrade pip

Install the basic system dependencies used by the codebase:

conda install -c conda-forge ffmpeg=7.1.1 svt-av1 -y

Install PyTorch for CUDA 12.8:

bash

pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 \
  --index-url https://download.pytorch.org/whl/cu128

Install Python dependencies:

bash

pip install torchcodec numpy scipy transformers==4.57.1 mediapy loguru pytest omegaconf
pip install -e .

3. Qwen3-VL Dependency

For CubeV2, the recommended dependency is the official Hugging Face Qwen3-VL implementation provided by transformers>=4.57.0.

In this repository, CubeV2 imports Qwen3-VL directly from:

python

from transformers.models.qwen3_vl import modeling_qwen3_vl
from transformers.models.qwen3_vl import Qwen3VLForConditionalGeneration, Qwen3VLTextModel

So for standard evaluation, you do not need to patch transformers if your environment already uses a recent enough official version such as transformers==4.57.1.

This repo also contains a vendored replacement file under:

src/lerobot/policies/cubev2/transformers_replace/models/qwen3_vl/modeling_qwen3_vl.py

That file is best understood as a repo-side override copy. Most users evaluating zaleni/MagicBot-VGA-Robotwin should not need it unless they intentionally want to reproduce a specific local patched behavior.

4. Prepare RoboTwin for Evaluation

This section is specifically for RoboTwin evaluation. If you only want to load the model or run other parts of the codebase, the extra RoboTwin setup below is not required.

Option A: initialize the bundled RoboTwin submodule

git submodule update --init third_party/RoboTwin

Option B: copy an existing RoboTwin checkout

You do not have to download RoboTwin from scratch if you already have a prepared copy. You can copy it into this repository instead.

The evaluation code assumes RoboTwin is located exactly at:

<repo_root>/third_party/RoboTwin

So a valid layout looks like:

text

MagicBot-VGA/
  evaluation/
  launch/
  src/
  third_party/
    RoboTwin/

If your RoboTwin directory already exists elsewhere, either:

copy it to third_party/RoboTwin, or
create a symlink at third_party/RoboTwin pointing to your existing RoboTwin directory

This path requirement comes from the evaluation code, which imports RoboTwin modules and task configs from third_party/RoboTwin directly.

Install RoboTwin-specific system dependency

RoboTwin rendering requires Vulkan:

sudo apt install -y libvulkan1 mesa-vulkan-drivers vulkan-tools

Install RoboTwin Python dependencies and assets

bash

cp evaluation/RoboTwin/requirements.txt third_party/RoboTwin/script/requirements.txt
cd third_party/RoboTwin
bash script/_install.sh
bash script/_download_assets.sh
cd ../../

For more RoboTwin installation details, you can also refer to the official documentation: https://robotwin-platform.github.io/doc/usage/robotwin-install.html

5. Prepare External Model Assets

The released checkpoint zaleni/MagicBot-VGA-Robotwin is intended to be lightweight. For RoboTwin action evaluation, you should provide the external backbone/tokenizer assets explicitly.

Recommended values:

Qwen3-VL backbone and processor: Qwen/Qwen3-VL-2B-Instruct
Cosmos tokenizer: nvidia/Cosmos-Tokenizer-CI8x8

You can use either:

public Hugging Face repo ids
local directories downloaded in advance

Example for offline/local usage:

bash

QWEN3_VL_PATH=/path/to/Qwen3-VL-2B-Instruct
COSMOS_TOKENIZER_PATH=/path/to/Cosmos-Tokenizer-CI8x8

For standard RoboTwin action evaluation, we recommend disabling DA3 teacher instantiation:

DISABLE_DA3_TEACHER_FOR_EVAL=true

This avoids loading the frozen DA3 teacher during evaluation while keeping the policy architecture compatible.

6. Single-Task Evaluation

The most direct way is to call evaluation/RoboTwin/inference.py on a single RoboTwin task.

Example: evaluate task 0 (adjust_bottle) on demo_clean:

bash

cd third_party/RoboTwin

python ../../evaluation/RoboTwin/inference.py \
  --args.ckpt_path zaleni/MagicBot-VGA-Robotwin \
  --args.video_dir ../../evaluation/RoboTwin/output_magicbot/demo_clean/task_00 \
  --args.task_config demo_clean \
  --args.task_idx 0 \
  --args.action_mode delta \
  --args.stats_key aloha \
  --args.dtype bfloat16 \
  --args.qwen3_vl_pretrained_path Qwen/Qwen3-VL-2B-Instruct \
  --args.qwen3_vl_processor_path Qwen/Qwen3-VL-2B-Instruct \
  --args.cosmos_tokenizer_path_or_name nvidia/Cosmos-Tokenizer-CI8x8 \
  --args.disable_3d_teacher_for_eval

If you use local asset directories, replace the public repo ids with your local paths.

Important arguments:

--args.ckpt_path: model repo id or local pretrained_model directory
--args.task_config: demo_clean or demo_randomized
--args.task_idx: task index in evaluation/RoboTwin/inference.py
--args.action_mode: usually delta for this model
--args.stats_key: usually aloha for RoboTwin
--args.dtype: bfloat16 is recommended on modern GPUs

Outputs are written to --args.video_dir, including:

replay videos
summary.json
summary.txt

7. 50-Task Randomized Evaluation

For batch evaluation on RoboTwin randomized tasks, use:

bash

PRETRAINED_CKPT=zaleni/MagicBot-VGA-Robotwin \
QWEN3_VL_PRETRAINED_PATH=Qwen/Qwen3-VL-2B-Instruct \
QWEN3_VL_PROCESSOR_PATH=Qwen/Qwen3-VL-2B-Instruct \
COSMOS_TOKENIZER_PATH_OR_NAME=nvidia/Cosmos-Tokenizer-CI8x8 \
DISABLE_DA3_TEACHER_FOR_EVAL=true \
GPU_IDS=0,1 \
MAX_JOBS_PER_GPU=2 \
bash evaluation/RoboTwin/eval_randomized_50.sh

Useful environment variables:

PRETRAINED_CKPT: model repo id or local checkpoint directory
GPU_IDS: comma-separated GPU ids, for example 0,1,2,3
MAX_JOBS_PER_GPU: parallel RoboTwin jobs per GPU
TASK_CONFIG: defaults to demo_randomized
TEST_NUM: number of episodes per task
DTYPE: bfloat16 or float32
BASE_OUTPUT_PATH: output root directory

This script writes:

per-task logs and videos under tasks/
aggregated summary.json
aggregated summary.txt

8. Evaluate a Continuous Task Range

eval_randomized_50.sh supports continuous ranges through:

START_TASK_IDX
TASK_COUNT

Example: evaluate tasks 10 to 19:

bash

PRETRAINED_CKPT=zaleni/MagicBot-VGA-Robotwin \
QWEN3_VL_PRETRAINED_PATH=Qwen/Qwen3-VL-2B-Instruct \
QWEN3_VL_PROCESSOR_PATH=Qwen/Qwen3-VL-2B-Instruct \
COSMOS_TOKENIZER_PATH_OR_NAME=nvidia/Cosmos-Tokenizer-CI8x8 \
DISABLE_DA3_TEACHER_FOR_EVAL=true \
START_TASK_IDX=10 \
TASK_COUNT=10 \
bash evaluation/RoboTwin/eval_randomized_50.sh

9. Evaluate the CVPR 2026 RoboTwin Track 11-Task Subset

For the Hugging Face leaderboard open-gigaai/CVPR-2026-RoboTwin-Track-LeaderBoard, we use the following 11-task subset:

[2, 3, 9, 10, 12, 15, 17, 25, 28, 30, 44]

The exact task names in evaluation/RoboTwin/inference.py are:

blocks_ranking_rgb
blocks_ranking_size
handover_mic
hanging_mug
move_can_pot
move_stapler_pad
open_microwave
place_can_basket
place_dual_shoes
place_fan
stack_blocks_three

The current batch script does not take a sparse task list directly, so the recommended approach is to run a shell loop:

bash

cd third_party/RoboTwin

TASKS=(2 3 9 10 12 15 17 25 28 30 44)
for t in "${TASKS[@]}"; do
  python ../../evaluation/RoboTwin/inference.py \
    --args.ckpt_path zaleni/MagicBot-VGA-Robotwin \
    --args.video_dir ../../evaluation/RoboTwin/output_magicbot/custom_subset/task_${t} \
    --args.task_config demo_randomized \
    --args.task_idx "${t}" \
    --args.action_mode delta \
    --args.stats_key aloha \
    --args.dtype bfloat16 \
    --args.qwen3_vl_pretrained_path Qwen/Qwen3-VL-2B-Instruct \
    --args.qwen3_vl_processor_path Qwen/Qwen3-VL-2B-Instruct \
    --args.cosmos_tokenizer_path_or_name nvidia/Cosmos-Tokenizer-CI8x8 \
    --args.disable_3d_teacher_for_eval
done

This produces one output directory per task, each containing replay videos plus summary.json and summary.txt.

10. Package the 11-Task Submission and Export Success Rates

After you finish the randomized evaluation run, you can convert those 11 tasks into a submission-style folder with:

bash

python util_scripts/package_robotwin_submission.py \
  --run /path/to/output_randomized_50/<run_name>/summary.txt \
  --dst /path/to/output_randomized_50/<run_name>/submission_package \
  --overwrite

If you also want to bundle a policy folder, add:

--policy-dir /path/to/policy/Your_Policy

The packaging script will:

create submission_package/<task_name>/episode0.mp4, episode1.mp4, ...
preserve the 11-task ordering by task index
write package_manifest.txt
write selected_task_summary.json
write selected_task_summary.txt

The selected-task summary files include:

per-task success_rate
per-task success_count and test_num
avg_task_success_rate across the 11 tasks
overall_episode_success_rate across all episodes in the 11-task subset

This is useful when you want a leaderboard-facing summary for the competition subset rather than the full randomized-50 report.

11. Task Index Reference

Task indices are defined in evaluation/RoboTwin/inference.py.

For example:

0: adjust_bottle
2: blocks_ranking_rgb
3: blocks_ranking_size
9: handover_mic
10: hanging_mug
12: move_can_pot
15: move_stapler_pad
17: open_microwave
25: place_can_basket
28: place_dual_shoes
30: place_fan
44: stack_blocks_three

12. Common Notes

inference.py can load checkpoints from either a local directory or a Hugging Face repo id.
If your server cannot access Hugging Face online, download the external assets in advance and pass local paths.
If you use the lightweight checkpoint release for action evaluation, keeping --args.disable_3d_teacher_for_eval enabled is recommended.
If you want to inspect reconstructed future images during inference, enable --args.decode_image_flag, though this is not required for standard RoboTwin scoring.

13. Model Link

Released RoboTwin checkpoint:

https://huggingface.co/zaleni/MagicBot-VGA-Robotwin

14. Acknowledgments

MagicBot-VGA is developed on top of the excellent InternVLA framework. Our codebase started from that foundation and has since been substantially modified and extended for our own model architecture, training pipeline, and evaluation workflow.

We sincerely thank the InternVLA authors and contributors for open-sourcing their framework and making follow-up research and development much easier.

We also thank the following open-source projects: