motor-system

Introduction

A code copied from google-research which named motion-imitation was rewrited with PyTorch. More details can get from this project.

GIthub Link:https://github.com/google-research/motion_imitation

Project Link:https://xbpeng.github.io/projects/Robotic_Imitation/index.html

Tutorials

For training：

python motion_imitation/run_torch.py --mode train --motion_file 'dog_pace.txt|dog_spin.txt' \
--int_save_freq 10000000 --visualize --num_envs 50 --type_name 'dog_pace'

• mode: train or test
• motion_file: Chose which motion to imitate (ps: | is used to split different motion)
• visualize: Whether rendering or not when training
• num_envs: Number of environments calculated in parallel
• type_name: Name of model file

For testing:

python motion_imitation/run_torch.py --mode test --motion_file 'dog_pace.txt' --model_file 'file_path' \ 
--encoder_file 'file_path' --visualize

• file_path: There's a model parameters zip file, you just find out and copy it's path.

Extra work

Adaptation

In this project, I donot use Gaussian distribution to fitting the encoder rather by using a mlp network with one hidden layer. The encoder loss function is -torch.sum(F.softmax(latent_param, dim=0) * advantages.reshape(-1, 1), dim=1).max(). Final loss function is policy + γ * encoder with optimized by Adam synchronously. Because there's no real robot, I do not transfer it to real world for testing.