Project Page | Video
This repo contains the implementation of our paper:
SoftGrasp: Adaptive Grasping for Dexterous Hand based on Multimodal Fusion Imitation Learning
YiHong Li,Ce Guo,JunKai Ren,Bailiang Chen,HuiZhang ,HuiMin Lu
conda create -n "SoftGrasp" python=3.7 -y && conda activate multimodal
pip install -r requirements.txt
To train policy with mutil_model
python SoftGrasp_train.py
Test mutil_model
python visualize_real.py
episode_times_1.csv
| file | Description |
|---|---|
| train.csv | train_dataset |
| val.csv | Val_dataset |
| test_recordings | dataset |
| exp_apple_21 | Fixed camera captures images |
| exp_apple_21.pickle | Contains human demonstration actions |
Here are what each symbol means:
| Symbol | Description |
|---|---|
| I | camera input from a fixed perspective |
| A | The end and finger joints of a robotic arm |
| T | Joint torque of dexterous hands |
To view your model's results, run
conda activate SoftGrasp
tensorboard --logdir exp{data}{task}
| Description | pmulsa |
|---|---|
| DATA | SoftGrasp_dataset.py |
| Dataset | base.py |
| ImiEngine | engine.py |
| imitation_model | SoftGrasp_models.py |