readme

README.md

@@ -19,13 +19,13 @@ Please refer to our 🚀 [Website](http://opendrivelab.com/freetacman) | 📄 [P
 ## 🔬 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).
+- **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.
+- **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.
+- **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.
+- **Simulation-to-Real Transfer**: Leverage the dynamic tactile interaction sequences to enhance tactile simulation fidelity, significantly reducing the sim2real gap.
 
 ## 📂 Dataset Structure