# Retarget Dataset
Chicken Dance
Jump Motion
We've brought more natural and interactive movements to the **Adamlite robot** by **retargeting motion data** from the **SFU dataset**, along with some of our **own captured and created motions**. This allows the robot to perform the natural walking and interactive behaviors you see in our public videos.
This was all made possible using a **whole-body inverse kinematics (IK) numerical optimization** approach. Our optimization focused on **joint position and joint velocity constraints** to ensure the movements are both realistic and stable. For some shorter actions, we've also **repeated the motion multiple times** to extend their usability.
## Requirements & System Specifications
To ensure the visualization and simulation run correctly, your system should meet the following requirements:
### System Requirements
- **OS**: Linux Ubuntu 18.04 / 20.04 / 22.04 (Isaac Gym does not support Windows/macOS natively).
- **GPU**: NVIDIA GeForce RTX 20 series or higher (RTX 3060+ recommended).
- **Driver**: NVIDIA Driver version >= 470.xx.
### Software Environment
- **Python**: 3.8 (Required for Isaac Gym Preview 4 compatibility).
- **Conda**: For environment management.
- **CUDA**: 11.7 (Aligned with the PyTorch version used).
- **Isaac Gym**: Preview 4 version.
## Trajectory Data Format
Our released trajectories come in a comprehensive **JSON format**. Each file provides:
- **Frame rate**: This tells you the time resolution of the motion.
- **Data labels**: These clearly identify the different data streams within the trajectory.
- **Trajectory data**: This is the core motion information, stored as a **2D array**.
Within this trajectory data, you'll find detailed insights into the robot's state and movement, including:
- **Base velocity**: The robot's linear and angular speed at its base.
- **Base pose**: The robot's position and orientation in space.
- **Joint angles**: The angular positions of each joint.
- **Joint velocities**: The angular speeds of each joint.
## Important Note on Execution
It's crucial to understand that these trajectories were developed with **only kinematic constraints** in mind, meaning we focused on the robot's physical structure and range of motion. **Dynamic considerations** (like forces and torques) were not included. Because of this, these trajectories **cannot be directly executed perfectly** on the robot without further dynamic control and adjustments.
# visualize robot trajectories
```shell
# Step 1: Create a new python virtual env with python 3.8
conda create -n retarget python=3.8
conda activate retarget
# Step 2: Install pytorch 1.13 with cuda-11.7:
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install numpy=1.23
# Step 3: Clone repository and Install IsaacGym
# 3.1 git clone
cd IsaacGym_Preview_4_Package/isaacgym/python && pip install -e .
# Step 4: Run the script
# run the script with parameters:
python visualize.py --robot_type=adam_lite --file_name mixamo/low_jump.json
python visualize.py --robot_type=adam_sp --file_name lafan1/walk4_subject1_extended.csv
```
# Dataset Collection Pipeline
# The Order of Configuration
Adam lite(23 DOF channels):
- root_pos/x
- root_pos/y
- root_pos/z
- root_quat/x
- root_quat/y
- root_quat/z
- root_quat/w
- dof_pos/hipPitch_Left
- dof_pos/hipRoll_Left
- dof_pos/hipYaw_Left
- dof_pos/kneePitch_Left
- dof_pos/anklePitch_Left
- dof_pos/ankleRoll_Left
- dof_pos/hipPitch_Right
- dof_pos/hipRoll_Right
- dof_pos/hipYaw_Right
- dof_pos/kneePitch_Right
- dof_pos/anklePitch_Right
- dof_pos/ankleRoll_Right
- dof_pos/waistRoll
- dof_pos/waistPitch
- dof_pos/waistYaw
- dof_pos/shoulderPitch_Left
- dof_pos/shoulderRoll_Left
- dof_pos/shoulderYaw_Left
- dof_pos/elbow_Left
- dof_pos/shoulderPitch_Right
- dof_pos/shoulderRoll_Right
- dof_pos/shoulderYaw_Right
- dof_pos/elbow_Right
- dof_vel/hipPitch_Left
- dof_vel/hipRoll_Left
- dof_vel/hipYaw_Left
- dof_vel/kneePitch_Left
- dof_vel/anklePitch_Left
- dof_vel/ankleRoll_Left
- dof_vel/hipPitch_Right
- dof_vel/hipRoll_Right
- dof_vel/hipYaw_Right
- dof_vel/kneePitch_Right
- dof_vel/anklePitch_Right
- dof_vel/ankleRoll_Right
- dof_vel/waistRoll
- dof_vel/waistPitch
- dof_vel/waistYaw
- dof_vel/shoulderPitch_Left
- dof_vel/shoulderRoll_Left
- dof_vel/shoulderYaw_Left
- dof_vel/elbow_Left
- dof_vel/shoulderPitch_Right
# About PND Adam
- The whole body is composed of up to 41 quasi-direct drive flexible force-controlled actuators.
- Adam Lite: 25 degrees of freedom.
- Adam Standard: 29 degrees of freedom.
- Adam SP: 41 degrees of freedom.
- Height: 1.67m, weight: 60kg.
- The legs use four quasi-direct drive force-controlled joints with the highest torque density in the industry and a 7-speed ratio and high sensitivity.
- The arms have up to 19 degrees of freedom.
- Adam Lite: 5 degrees of freedom.
- Adam Standard: 7 degrees of freedom.
- Adam SP: 19 degrees of freedom.
- The waist has 3 degrees of freedom.
# Introduction to the Appearance of Humanoid Robots
## Names of Main Components
# FAQ
ImportError: libpython3.8.so.1.0: cannot open shared object file: No such file or directory
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib