The implementation of QuasiSim, presenting a parameterized family of quasi-physical simulators for transferring kinematics-only human manipulation demonstrations to dexterous robot hand simulations.

We enable accurately controlling a simulated dexterous robot hand to track complex human manipulations with changing contact, non-trivial object motions, and intricate tool-using in a physically realistic simulated environment.

The repository contains

• Analytical part of the parameterized quasi-physical simulator;
• Detailed instructions on the optimization process for a manipulation sequence example (first two stages);
• Hybrid part with PyBullet;

We will add the remaining code and instructions, as well as the data and more manipulation examples.

• 🤗 2024/07/07: Analytical-PyBullet hybrid part has been released.
• 🎉 2024/07/02: QuasiSim is accpeted to ECCV 2024! See you in Milano~
• 🤖 2024/05/17: We've conducted real robot trajectory replay expeirments on an Allegro Hand to validate the real world applicability of QuasiSim. Check the website for videos. Very grateful for the hardward support from Prof. Rui Chen's team!
• 🚀 2024/04/15: First version code released!

This code was tested on Ubuntu 20.04.5 LTS and requires:

• Python 3.8.8
• conda3 or miniconda3
• CUDA capable GPU (one is enough)

1. Create a virtual environment and install necessary dependencies

Create a virtual environment

conda create -n quasisim python==3.8.8
conda activate quasisim

2. Install torch2.2.0+cu121

conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia

3. Install torch_cluster:

mkdir whls
cd whls
wget https://data.pyg.org/whl/torch-2.2.0%2Bcu121/torch_cluster-1.6.3%2Bpt22cu121-cp38-cp38-linux_x86_64.whl
pip install torch_cluster-1.6.3+pt22cu121-cp38-cp38-linux_x86_64.whl

4. Install other dependences

pip install -r requirements.txt

5. Setup DiffHand:

cd DiffHand
cd core
python setup.py install
### For testing the installation ###
cd ..
cd examples
python test_redmax.py

Opting to install it from this project is preferable, as we've made modifications to align with our specific objectives.

Examples data

Download the example data and organize them in the same way as we do in OnDrive. The expected file structure is as follows:

data
 |-- grab
     |-- 102
         |-- 102_obj.npy
         |-- 102_obj.obj
         |-- 102_sv_dict.npy
         |-- 102_sv_dict_st_0_ed_108.npy
     ...

Download the ReferenceData and put them in the folder ./ReferenceData.

Checkpoints

Download pre-optimized checkpoints and organize them in the same way as we do in OnDrive. The expected file structure is as follows:

ckpts
 |-- grab
     |-- 102
         |-- xxx1.pt
         |-- xxx2.pt
     ...

URDFs and others

Download rsc.zip and raw_data.zip. Extract them in the root folder of the project.

Important: Download mano_pts.zip. Extract them in the folder rsc/mano.

We provide the data, detailed instructions, and the results of a specific example with the aim of elucidating the inverse dynamics problem within contact-rich manipulation scenarios, which we address leveraging the physics curriculum programmed from QuasiSim. Currently, we release the analytical part of QuasiSim as well as the first two optimization stages. The thrid stage, alongside with more data and their optimization code will be added.

In the case of the example sequence data/grab/102, which depicts a human hand rotating a mouse, the human manipulation demonstration, transferred manipulation to the simulated Shadow hand in the QuasiSim's analytical environment, and the manipulation optimized in the Bullet simulator are shown as follows.

Human Manipulation	Transferred to Shadow	Transferred to Shadow in Bullet

The following instructions aim to optimize the control trajectory to enable the Shadow hand to complete the tracking task, as demonstrated in the middle demo, within the stiffest analytical environment of QuasiSim.

We save checkpoints with robot poses in the folder checkpoints of the experiment root folder and mesh/point cloud information in the folder meshes.

Transferring human demonstrations via point set dynamics. In this stage, we represent the MANO model and the Shadow model within the simulation as parameterized point sets. The contact model is adjusted to its softest level. The objective is to optimize a trajectory of point sets for the Shadow hand to successfully accomplish manipulation tracking.

This stage is divided into four steps as described below.

By default, the experiment directory where the intermediate optimization results and checkpoints are saved is exp/. However, considering that the checkpoints will occupy a considerable amount of storage, it's advisable to change it to a folder designated for storing large files. To use a different experimental folder, set the variable local_exp_dir in the __init__ function of the Runner class within the files exp_runner_stage_1.py and exp_runner_stage_2.py to your desired folder.

Step 1: Optimizing the trajectory of the simulated dynamic MANO hand

In this step, we optimize a control trajectory for the dynamic MANO hand model to track the reference manipulation. Please execute the following commands sequentially:

Substep 1: Optimize for a MANO dynamic trajectory to track the parametric MANO trajectory

bash scripts_new/train_grab_mano.sh # substep 1

For visualizing the optimized MANO hand meshes together with the object sequences, find the retargeting_info_${epoch_idx}.npy in the ${exp_folder}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized retargeting_info_xxx file.

Substep 2: Identify system parameters

To utilize the optimized checkpoint from the previous substep, adjust the arguments ckpt_fn and load_optimized_init_actions in the file confs_new/dyn_grab_pointset_mano_dyn.conf to point to the last one saved in the previous substep (e.g., exp/_reverse_value_totviews_tag_train_dyn_mano_acts_/checkpoints/ckpt_054060.pth). Alternatively, leave these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_mano_wreact.sh # substep 2

For visualizing the optimized MANO hand meshes together with the object sequences, find the hand_obj_verts_faces_sv_dict_${epoch_idx}.npy in the ${exp_folder}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized hand_obj_verts_faces_sv_dict_xxx file.

Substep 3: Optimize for a MANO dynamic trajectory to physically track the demonstration

To utilize the optimized checkpoint from the preceding substep, adjust the arguments ckpt_fn and load_optimized_init_actions in the file confs_new/dyn_grab_pointset_mano_dyn_optacts.conf to correspond to the latest one saved in the preceding substep. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_mano_wreact_optacts.sh # substep 3

For visualizing the optimized MANO hand meshes together with the object sequences, find the hand_obj_verts_faces_sv_dict_${epoch_idx}.npy in the ${exp_folder}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized hand_obj_verts_faces_sv_dict_xxx file.

The time consumption for each substep is listed below, as we tested:

Step 2: Optimizing the control trajectory for the point set constructed from the MANO hand

Execute the following four substeps. Please note that they cannot be excuted in parallel.

Substep 1: Kinematic MANO's point set trajectory optimization

To utilize the optimized checkpoint from the preceding substep, adjust the argument load_optimized_init_actions in the file confs_new/dyn_grab_pointset_points_dyn_s1.conf to correspond to the latest one saved in the preceding step. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_s1.sh # substep 1 

Substep 2: Dynamic MANO's point set trajectory optimization

To utilize the optimized checkpoint from the preceding substep, adjust the argument load_optimized_init_actions in the file confs_new/dyn_grab_pointset_points_dyn_s2.conf to correspond to the latest one saved in the preceding step. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_s2.sh # substep 2

Substep 3: Identify the undefined parameters

To utilize the optimized checkpoint from the preceding substep, adjust the argument load_optimized_init_actions in the file confs_new/dyn_grab_pointset_points_dyn_s3.conf to correspond to the latest one saved in the preceding step. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_s3.sh # substep 3

Substep 4: Dynamic MANO's point set trajectory optimization

To utilize the optimized checkpoint from the preceding substep, adjust the arguments ckpt_fn and load_optimized_init_actions in the file confs_new/dyn_grab_pointset_points_dyn_s4.conf to correspond to the latest one saved in the preceding step. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_s4.sh # substep 4

In each substep, for visualizing the optimized MANO point set together with the object sequences, find the hand_obj_verts_faces_sv_dict_${epoch_idx}.npy in the ${exp_folder}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized hand_obj_verts_faces_sv_dict_xxx file.

The time consumption for each substep is listed below, as we tested:

As for the way of creating the point set representation from an articulated rigid mesh, we sample nn_expand_pts uniformly randomly from a ball with the radius controlled by expand_factor constructed around each surface point. The point set is then formed by concatenating all such points together. By default, nn_expand_pts is set to 10, while expand_factor is 0.1. Increasing the two parameters will result in a larger point set. Try to adjust them through .conf file. We've tested nn_expand_pts=10;expand_factor=0.1, nn_expand_pts=20;expand_factor=0.2, nn_expand_pts=40;expand_factor=0.4, and nn_expand_pts=80;expand_factor=0.4. More analysis on them will be added.

Step 3: Optimizing the trajectory of the kinematic Shadow hand

In this step, we optimize the trajectory of the kinematic Shadow hand based on keypoint-based correspondences and mesh surface point-based correspondences. Execute the following command for this step:

bash scripts_new/train_grab_sparse_retar.sh

The time consumption for this step is about 7 hrs as we tested on a single A800 gpu.

For visualizing the optimized Shadow hand mesh together with the object sequences, find the ${epoch_idx}.npy in the ${exp_folder}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized ${epoch_idx}.npy file.

Step 4: Optimizing the control trajectory for the point set constructed from the simulated Shadow hand

Please execute the following four substeps for this step:

Substep 1: Track MANO's point set via the Shadow hand

To utilize the optimized checkpoint from the preceding substep, adjust the arguments in the file confs_new/dyn_grab_pointset_points_dyn_retar.conf as follows:

• set ckpt_fn and load_optimized_init_actions to the path of the last saved model in Step 2;
• set load_optimized_init_transformations to the path of the last optimized model in Step 3. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_retar.sh

Substep 2: Track MANO's point set via the Shadow's point set

To utilize the optimized checkpoint from the preceding substep, adjust the arguments in the file confs_new/dyn_grab_pointset_points_dyn_retar_pts.conf as follows:

• set ckpt_fn and load_optimized_init_actions to the path of the last saved model in Step 2;
• set dyn_grab_pointset_points_dyn_retar_pts to the path of the last optimized model in the previous substep. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_retar_pts.sh

Substep 3: System parameters identification

To utilize the optimized checkpoint from the preceding substep, adjust the arguments in the file confs_new/dyn_grab_pointset_points_dyn_retar_pts.conf as follows:

• set ckpt_fn and load_optimized_init_actions to the path of the last saved model in Step 2;
• set dyn_grab_pointset_points_dyn_retar_pts to the path of the last optimized model in the previous substep. Alternatively, retain these arguments unchanged to utilize our pre-optimized checkpoint.

Run

bash scripts_new/train_grab_pointset_points_dyn_retar_pts_opts.sh

In each substep, for visualizing the optimized Shadow point set together with the object sequences, find the hand_obj_verts_faces_sv_dict_${epoch_idx}.npy in the ${exp_folder}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized hand_obj_verts_faces_sv_dict_xxx file.

The time consumption for each substep is listed below, as we tested:

Please note that the point set constructed from the simulated Shadow hand contains over 100,000 points. Running the last two substeps necessitates a GPU memory of at least 56GB. Consequently, in our experiments, this part is conducted using 80GB A800 GPUs.

An alternative approach is optimizing the trajectory of the articulated Shadow hand directly.

This option requires a smaller GPU memory size. A single 3090/4090 with 24GB is sufficient.

For the example currently included in the repository with bulky object shapes and relatively clean human motion references, this strategy can yield compatible optimization results compared to the original approach. However, it may lead to degradation in some cases. Please execute the following commands for this optimization suite:

bash scripts_new/train_grab_pointset_points_dyn_retar.sh
bash scripts_new/train_grab_pointset_points_dyn_retar_opts.sh

Tracking via a contact curriculum. In this stage, we optimize the control trajectory of the simulated Shadow hand to complete the tracking task through a curriculum of contact models. Initially, the contact model is tuned to the softest level. Subsequently, we gradually adjust parameters to tune it to the stiffest level.

Run the following command:

bash scripts_new/train_grab_stage_2_dm_curriculum.sh

In each stage, for visualizing the optimized Shadow hand meshe and the object sequences, find the hand_obj_verts_faces_sv_dict_${epoch_idx}.npy in the ${exp_folder}/${curriculum_stage_name}/meshes folder saved from the last optimization loop (with the largest epoch_idx) and run the following command:

python visualize/vis_tracking.py --tracking_info_fn=${saved_tracking_info_fn}

where saved_tracking_info_fn should be set to the last saved optimized hand_obj_verts_faces_sv_dict_xxx file.

Tracking via iterative hybrid sim model training and trajectory optimization

Further Installations:

• mpi4py:

  # may require a system package dependency
  sudo apt install libopenmpi-dev
  # then install the package using pip
  pip install mpi4py
  # if this can still not work, try to install it using conda
  conda install -c conda-forge mpi4py mpich

• pybullet:

  pip install pybullet

Run the following command:

bash scripts_new/run_train_policy_hoi_wana.sh

• Analytical part of QuasiSim
• Optimization example
• Hybrid part of QuasiSim with PyBullet
• Remaining code of the hybrid part
• More examples
• Custimizing your optimization

Please contact xymeow7@gmail.com or raise a github issue if you have any questions.

If you find this code useful in your research, please cite:

@article{liu2024quasisim,
  title={QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer},
  author={Liu, Xueyi and Lyu, Kangbo and Zhang, Jieqiong and Du, Tao and Yi, Li},
  journal={arXiv preprint arXiv:2404.07988},
  year={2024}
}

This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: DiffHand, NeuS, ControlVAE, and UniDexGrasp.

We thank Prof. Rui Chen, Yixuan Guan, and Taoran Jiang for their valuable support in providing the Allegro hardware and setting up the environment setup during the rebuttal period.

This code is distributed under an MIT LICENSE.