update the comparative experiments on the RoboTwin 2.0 benchmark

README.md

@@ -6,7 +6,7 @@ tags:
 - robotics
 - vision-language-action-model
 datasets:
-- InternRobotics/InternData-A1
+- hxma/RoboTwin-LeRobot-v3.0
 ---
 
 # InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
@@ -31,6 +31,39 @@ Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B p
 - [ ] [InternVLA-A1-3B-Pretrain-InternData-A1](https://huggingface.co/InternRobotics/InternVLA-A1-3B-Pretrain-InternData-A1): pretrained on InternData-A1 only
 - [ ] [InternVLA-A1-2B-Pretrain-InternData-A1](https://huggingface.co/InternRobotics/InternVLA-A1-2B-Pretrain-InternData-A1): pretrained on InternData-A1 only
 
+
+## **Evaluation on RoboTwin 2.0 Simulation Benchmark**
+
+**Setting:** All models are jointly fine-tuned across 50 tasks (50 clean + 500 randomized demos each).
+
+**Performance Summary:** InternVLA-A1-3B achieves the highest success rates across both Easy and Hard settings on the RoboTwin 2.0 Benchmark (averaged over 50 tasks).
+
+| Metric | $\pi_0$ | $\pi_{0.5}$ | **InternVLA-A1-3B** |
+| :--- | :---: | :---: | :---: |
+| **Avg. Success (Easy)** | 79.98% | 84.70% | **88.30%** 🥇 |
+| **Avg. Success (Hard)** | 79.50% | 85.02% | **88.48%** 🥇 |
+
+<details>
+<summary>🔻 <b>Click to view detailed results for specific tasks</b></summary>
+
+<br>
+
+The table below shows success rates formatted as <code>Easy / Hard</code>.
+
+| Task Name | $\pi_0$ | $\pi_{0.5}$ | **InternVLA-A1-3B** |
+| :--- | :---: | :---: | :---: |
+| **Click Bell** | 70.0% / 69.0% | **97.0%** / 93.0% | **97.0%** / **94.0%** |
+| **Move Pillbottle Pad** | 83.0% / 82.0% | 92.0% / 89.0% | **95.0%** / **99.0%** |
+| **Open Laptop** | 90.0% / 97.0% | 92.0% / 97.0% | **99.0%** / **99.0%** |
+| **Handover Block** | 70.0% / 53.0% | 60.0% / 59.0% | **87.0%** / **81.0%** |
+| **Blocks Ranking Size** | 59.0% / 57.0% | 73.0% / 77.0% | **82.0%** / **92.0%** |
+| **Place Dual Shoes** | 69.0% / 76.0% | 57.0% / 65.0% | **93.0%** / **85.0%** |
+| **Stamp Seal** | 62.0% / 65.0% | 66.0% / **73.0%** | **71.0%** / 71.0% |
+| **Stack Bowls Three** | 81.0% / 75.0% | **88.0%** / 85.0% | 86.0% / **95.0%** |
+
+</details>
+
+
 ## 🔑 Key Features
 
 Regarding model architecture, InternVLA-A1 employs a Mixture-of-Transformers (MoT) design to unifies scene understanding, visual foresight, and action execution into a single framework.