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Omnigrasp: Grasping Diverse Objects with Simulated Humanoids

Official implementation of NeurIPS 2024 paper: "Omnigrasp: Grasping Diverse Objects with Simulated Humanoids". In this project, we control a simulated humanoid to grasp diverse objects and follow diverse object trajectories.

[paper] [website]

News 🚩

[December 12, 2024] Release inference code and models.

TODOs

  • Release character drawing code.
  • Release training code (mainly the data processing part).
  • Release inference code.

Dependencies

To create the environment, follow the following instructions:

  1. Create new conda environment and install pytorch:
conda create -n isaac python=3.8
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
pip install -r requirement.txt

  1. Download and setup Isaac Gym.

  2. Download SMPL paramters from SMPL and SMPLX. Put them in the data/smpl folder, unzip them into 'data/smpl' folder. For SMPL, please download the v1.1.0 version, which contains the neutral humanoid. Rename the files basicmodel_neutral_lbs_10_207_0_v1.1.0, basicmodel_m_lbs_10_207_0_v1.1.0.pkl, basicmodel_f_lbs_10_207_0_v1.1.0.pkl to SMPL_NEUTRAL.pkl, SMPL_MALE.pkl and SMPL_FEMALE.pkl. For SMPLX, please download the v1.1 version. Rename The file structure should look like this:


|-- data
    |-- smpl
        |-- SMPLX_FEMALE.pkl
        |-- SMPLX_NEUTRAL.pkl
        |-- SMPLX_MALE.pkl

Make sure you have the SMPL-X paramters properly setup by running the following scripts:

python scripts/vis/vis_motion_mj.py
python scripts/joint_monkey_smpl.py

The SMPL model is used to adjust the height the humanoid robot to avoid penetnration with the ground during data loading.

  1. Use the following script to download trained models and sample data.
bash download_data.sh

Evaluation

Viewer Shortcuts

Keyboard Function
f focus on humanoid
Right click + WASD change view port
Shift + Right click + WASD change view port fast
r reset episode
j apply large force to the humanoid
l record screenshot, press again to stop recording
; cancel screen shot
m cancel termination based on imitation

... more shortcut can be found in phc/env/tasks/base_task.py

Inference on GRAB objects + random trajectories

To evaluate a trained policy on the GRAB dataset, run the following script:

python phc/run_hydra.py  \
    project_name=OmniGrasp   exp_name=omnigrasp_neurips_grab  \
    learning=omnigrasp_rnn \
    env=env_x_grab_z env.task=HumanoidOmniGraspZ env.motion_file=sample_data/grab_sample.pkl  env.models=['output/HumanoidIm/pulse_x_omnigrasp/Humanoid.pth']   env.numTrajSamples=20 env.trajSampleTimestepInv=15\
    robot=smplx_humanoid sim=hand_sim  \
    epoch=-1 test=True env.num_envs=45 headless=False

Rendering the above trajectory might be slow, to speed up, use less number of environments or disable object trajectory rendering by press "k".

Tracing words in air

[Coming soon]

Training

[This section is under construction]

Data processing:

To train Omnigrasp, we need to process the GRAB dataset. Notice that while Omnigrasp does not depend on the GRAB dataset (we train on oakink and OMOMO as well), the GRAB dataset is used as a pre-grasp provider and inital pose provider. When training on oakink/OMOMO, a single sample motion from GRAB is used as the initial pose.

To setup the GRAB dataset, please follow the following instructions:

  1. Download the GRAB dataset from here.
  2. Run the following script to process the dataset:
python scripts/data_process/convert_grab_smplx.py