Multi-Keypoint Affordance Representation for Functional Dexterous Grasping (MKA)

RAL-25-0680.mp4

Usage

Affordance (CMKA)

1. Requirements

Code is tested under Pytorch 1.12.1, python 3.7, and CUDA 11.6

pip install -r funcgra/requirements.txt

You can download the sam_vit modles (Baidu Pan). The extraction code is: ca8a.

2. Dataset

You can download the FAH from Baidu Pan (3.35G). The extraction code is: a6nv.

3. Train

Run following commands to start training:

python train.py --data_root <PATH_TO_DATA>

####4. You can download the pretrained modles Baidu Pan). The extraction code is: 8qvk.

Simulation (KGT)

1. Requirements

The simulation code was tested on Pytorch 1.13.1, Python 3.7, and CUDA 9.1

pip install -r requirements_sim.txt

2. Run

cd isaacgym/python
python hand_move_to_drill_hold_no_pengzhuang.py
python hand_move_to_drill_press_test.py
python hand_move_to_flashlight_click
python hand_move_to_flashlight_hold.py

Experiments

1. Requirements

The experiments code was tested on Pytorch 1.12.0, Python 3.9, and CUDA 11.3

pip install -r requirements_exp.txt

2. Run

cd fungra/
python robot_control_click_the_flashlight.py
python robot_control_press_the_spraybottle.py
python robot_control_press_the_drill.py
python robot_control_hold_the_kettle.py