readme

README.md

@@ -16,6 +16,17 @@ This dataset supports the paper **[FreeTacman: Robot-free Visuo-Tactile Data Col
 ![FreeTacMan System Overview](https://raw.githubusercontent.com/OpenDriveLab/opendrivelab.github.io/master/FreeTacMan/task/datasetweb.png)
 Please refer to our 🚀 [Website](http://opendrivelab.com/freetacman) | 📄 [Paper](http://arxiv.org/abs/2506.01941) | 💻 [Code](https://github.com/OpenDriveLab/FreeTacMan) | 🛠️ [Hardware Guide](https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit?addon_store&tab=t.0#heading=h.rl14j3i7oz0t) | 📺 [Video](https://opendrivelab.github.io/FreeTacMan/landing/FreeTacMan_demo_video.mp4) | 🌐 [X](https://x.com/OpenDriveLab/status/1930234855729836112) for more details.
 
+## 🔬 Potential Applications
+The FreeTacman dataset enables diverse research directions in visuo-tactile learning and manipulation:
+
+**System Reproduction**: For researchers interested in hardware implementation, you can reproduce FreeTacMan from scratch using our 🛠️ [Hardware Guide](https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit?addon_store&tab=t.0#heading=h.rl14j3i7oz0t) and 💻 [Code](https://github.com/OpenDriveLab/FreeTacMan).
+
+**Multimodal Imitation Learning**: Transfer to other LED-based tactile sensors (such as GelSight) for developing robust multimodal imitation learning frameworks.
+
+**Tactile-aware Grasping**: Utilize the dataset for pre-training tactile representation models and developing tactile-aware reasoning systems.
+
+**Simulation-to-Real Transfer**: Leverage the dynamic tactile interaction sequences to enhance tactile simulation fidelity, significantly reducing the sim2real gap.
+
 ## 📂 Dataset Structure
 
 The dataset is organized into 50 task categories, each containing: