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🧪 Evaluation on RoboTwin-2.0

We evaluate X-VLA on the RoboTwin-2.0 benchmark to assess its ability to handle bimanual tabletop manipulation with multiple object sets, shifting layouts, and varied visual randomness.

1️⃣ Environment Setup

Follow the official instructions from the original RoboTwin-2.0 repository:
👉 https://robotwin-platform.github.io/doc/usage/index.html

No additional modifications are required for X-VLA evaluation.

2️⃣ Start the X-VLA Server

Run the X-VLA model as an inference server (in a clean environment to avoid dependency conflicts):

conda activate X-VLA
python -m deploy --model_path 2toINF/X-VLA-RoboTwin2

3️⃣ Run the Client Evaluation

Add the absolute path of your RoboTwin repository at line 4 of X-VLA/evaluation/robotwin-2.0/client.py:

robowin_root = Path("/home/dodo/fyc/RoboTwin") # <- Add your path

Launch the RoboTwin-2.0 evaluation client to connect to your X-VLA server:

cd evaluation/robotwin-2.0
bash eval_robotwin.sh

You can configure custome evaluation in eval_robotwin.sh, such as log directry, server port number, number of episodes evaluated, task config, etc.

The client will stream observations (images, proprioception, and language) to the X-VLA model, receive predicted actions, and execute them within the RoboTwin-2.0 environment.

📊 Results (Using RoboTwin-2.0 Leaderboard Settings)

Settings Easy Hard
Success (%) 70.0 39.0