Hugging Face's logo

Datasets:
unitreerobotics
/
LAFAN1_Retargeting_Dataset

Tasks:
Robotics
Modalities:
3D
Dataset card Viewer Files Community
2
rerun2023 commited on
Commit
e11da8f
·
verified ·
1 Parent(s): fc60903

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +7 -2
README.md CHANGED
@@ -1,5 +1,6 @@
1
  ---
2
- license: bsd-3-clause-clear
 
3
  ---
4
 
5
  # LAFAN1 Retargeting Dataset
@@ -8,7 +9,7 @@ license: bsd-3-clause-clear
8
 
9
  To make the motion of humanoid robots more natural, we retargeted [LAFAN1](https://github.com/ubisoft/ubisoft-laforge-animation-dataset) motion capture data to [Unitree](https://www.unitree.com/)'s humanoid robots, supporting three models: [H1, H1_2](https://www.unitree.com/h1), and [G1](https://www.unitree.com/g1). This retargeting was achieved through numerical optimization based on [Interaction Mesh](https://ieeexplore.ieee.org/document/6651585) and IK, considering end-effector pose constraints, as well as joint position and velocity constraints, to prevent foot slippage. It is important to note that the retargeting only accounted for kinematic constraints and did not include dynamic constraints or actuator limitations. As a result, the robot cannot perfectly execute the retargeted trajectories.
10
 
11
- **How to visualize robot trajectories?**
12
 
13
  ```shell
14
  # Step 1: Set up a Conda virtual environment
@@ -25,6 +26,10 @@ python rerun_visualize.py
25
  # python rerun_visualize.py --file_name dance1_subject2 --robot_type [g1|h1|h1_2]
26
  ```
27
 
 
 
 
 
28
  This database stores the retargeted trajectories in CSV format. Each row in the CSV file corresponds to the original motion capture data for each frame, recording the configurations of all joints in the humanoid robot in the following order:
29
 
30
  ```txt
 
1
  ---
2
+ task_categories:
3
+ - robotics
4
  ---
5
 
6
  # LAFAN1 Retargeting Dataset
 
9
 
10
  To make the motion of humanoid robots more natural, we retargeted [LAFAN1](https://github.com/ubisoft/ubisoft-laforge-animation-dataset) motion capture data to [Unitree](https://www.unitree.com/)'s humanoid robots, supporting three models: [H1, H1_2](https://www.unitree.com/h1), and [G1](https://www.unitree.com/g1). This retargeting was achieved through numerical optimization based on [Interaction Mesh](https://ieeexplore.ieee.org/document/6651585) and IK, considering end-effector pose constraints, as well as joint position and velocity constraints, to prevent foot slippage. It is important to note that the retargeting only accounted for kinematic constraints and did not include dynamic constraints or actuator limitations. As a result, the robot cannot perfectly execute the retargeted trajectories.
11
 
12
+ # How to visualize robot trajectories?
13
 
14
  ```shell
15
  # Step 1: Set up a Conda virtual environment
 
26
  # python rerun_visualize.py --file_name dance1_subject2 --robot_type [g1|h1|h1_2]
27
  ```
28
 
29
+ # Dataset Collection Pipeline
30
+
31
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67639932ad38702e6c8d16d9/_tAr3zwPotJGaUqxe2u4p.png)
32
+
33
  This database stores the retargeted trajectories in CSV format. Each row in the CSV file corresponds to the original motion capture data for each frame, recording the configurations of all joints in the humanoid robot in the following order:
34
 
35
  ```txt