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README.md

@@ -4,14 +4,14 @@ license: apache-2.0
 # Aligning Touch, Vision, and Language for Multimodal Perception
 by <a href="https://max-fu.github.io">Max (Letian) Fu</a>, <a href="https://www.linkedin.com/in/gaurav-datta/">Gaurav Datta*</a>, <a href="https://qingh097.github.io/">Huang Huang*</a>, <a href="https://autolab.berkeley.edu/people">William Chung-Ho Panitch*</a>, <a href="https://www.linkedin.com/in/jaimyn-drake/">Jaimyn Drake*</a>, <a href="https://joeaortiz.github.io/">Joseph Ortiz</a>, <a href="https://www.mustafamukadam.com/">Mustafa Mukadam</a>, <a href="https://scholar.google.com/citations?user=p6DCMrQAAAAJ&hl=en">Mike Lambeta</a>, <a href="https://lasr.org/">Roberto Calandra</a>, <a href="https://goldberg.berkeley.edu">Ken Goldberg</a> at UC Berkeley, Meta AI, and TU Dresden (*equal contribution).
 
-[[Paper](#todo)] | [[Project Page](https://tvl.github.io/)] | [[Citation](#citation)]
+[[Paper](#todo)] | [[Project Page](https://tactile-vlm.github.io/)] | [[Citation](#citation)]
 
 <p align="center">
   <img src="img/splash_figure_alt.png" width="800">
 </p>
 
 
-This repo contains the official checkpoints for *Aligning Touch, Vision, and Language for Multimodal Perception*. 
+This repo contains the official checkpoints for *A Touch, Vision, and Language Dataset for Multimodal Perception*. 
 
 The tactile encoders comes in three different sizes: ViT-Tiny, ViT-Small, and ViT-Base, all of which are stored in
 ```bash
@@ -32,4 +32,4 @@ CLIP_PRETRAIN_DATA = "datacomp_xl_s13b_b90k"
 
 For TVL-LLaMA, please request access to the pre-trained LLaMA-2 from this [form](https://llama.meta.com/llama-downloads/). In particular, we use `llama-2-7b` as the base model. The weights here contains the trained [adapter](https://arxiv.org/abs/2309.03905), the tactile encoder, and the vision encoder for the ease of loading. 
 
-For the complete info, please take a look at the [GitHub repo](https://tvl.github.io) to see instructions on pretraining, fine-tuning, and evaluation with these models.
+For the complete info, please take a look at the [GitHub repo](https://tactile-vlm.github.io/) to see instructions on pretraining, fine-tuning, and evaluation with these models.