Update README.md

README.md

@@ -8,10 +8,19 @@ task_categories:
 <h1> ALMI-X </h1>
 </div>
 <h5 align="center">
-    <a href="#"> coming soon :) ...</a>
+    <a href="https://almi-humanoid.github.io/">🕺🏻demo webpage</a>
 </h5>
 
-# Introduction of Dataset
+
+# Overview
+We release a large-scale whole-body motion control dataset - ALMI-X, featuring high-quality episodic trajectories from MuJoCo simulations deployable on real robots, based on our humanoid control policy - ALMI.
+
+# Dataset Instruction
+We collect ALMI-X dataset in MuJoCo simulation by running the trained ALMI policy. In this simulation, we combine a diverse range of upper-body motions with omnidirectional lower-body commands, and employ a pre-defined paradigm to generate corresponding linguistic descriptions for each combination. (i) For the upper-body, we collect data using our upper-body policy to track various motions from a subset of the AMASS dataset, where we remove entries with indistinct movements or those that could not be matched with the lower-body commands, such as `push from behind`. 
+(ii) For the lower-body, we first categorize command directions into several types according to different combination of linear and angular velocity command and define 3 difficulty levels for command magnitudes, then the lower-body command is set by combining direction types and difficulty levels. 
+    Overall, each upper-body motion from the AMASS subset is paired with a specific direction type and a difficulty level serving as the inputs of policy to control the robot. In addition, trajectories in which the lower body `stand still` while the upper body tracks motions are also incorporated into the dataset. Each language description in ALMI-X is organized as `"[movement mode] [direction] [velocity level] and `motion`"}, each of which corresponds to the data collected from a trajectory lasting about 4 seconds with 200 steps. For each trajectory$, we run two policies (i.e., lower policy and upper policy) based on the commands obtained from the aforementioned combinations to achieve humanoid whole-body control.
+ 
+# How to Use Dataset
 
  - We release all of the text description data `text.tar.gz`; the trajectory data `data.tar.gz` with robot states, actions, DoF position, global position and global orientation informations.
  - We release the train set split `train.txt`