Update README.md

README.md

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+
+# Visual Dexterity
+
+---
+
+This is the codebase for [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://arxiv.org/abs/2211.11744), accepted by Science Robotics. While we provide the code that uses the D'Claw robot hand, it can be easily adapted to other robot hands.
+
+### [[Project Page]](https://taochenshh.github.io/projects/visual-dexterity), [[Science Robotics]](https://www.science.org/doi/10.1126/scirobotics.adc9244), [[arXiv]](https://arxiv.org/abs/2211.11744), [[Github]](https://github.com/Improbable-AI/dexenv)
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10039109.svg)](https://doi.org/10.5281/zenodo.10039109)
+
+
+## :books: Citation
+
+```
+@article{chen2023visual,
+    author = {Tao Chen  and Megha Tippur  and Siyang Wu  and Vikash Kumar  and Edward Adelson  and Pulkit Agrawal },
+    title = {Visual dexterity: In-hand reorientation of novel and complex object shapes},
+    journal = {Science Robotics},
+    volume = {8},
+    number = {84},
+    pages = {eadc9244},
+    year = {2023},
+    doi = {10.1126/scirobotics.adc9244},
+    URL = {https://www.science.org/doi/abs/10.1126/scirobotics.adc9244},
+    eprint = {https://www.science.org/doi/pdf/10.1126/scirobotics.adc9244},
+}
+```
+
+```
+@article{chen2021system,
+    title={A System for General In-Hand Object Re-Orientation},
+    author={Chen, Tao and Xu, Jie and Agrawal, Pulkit},
+    journal={Conference on Robot Learning},
+    year={2021}
+}
+```
+
+## :gear: Installation
+
+#### Dependencies
+* [PyTorch](https://pytorch.org/)
+* [PyTorch3D](https://pytorch3d.org/)
+* [Isaac Gym](https://developer.nvidia.com/isaac-gym) (results in the paper are trained with Preview 3.)
+* [IsaacGymEnvs](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs)
+* [Minkowski Engine](https://github.com/NVIDIA/MinkowskiEngine)
+* [Wandb](https://wandb.ai/site)
+
+
+#### Download packages
+You can either use a virtual python environment or a docker for training. Below we show the process to set up the docker image. If you prefer using a virtual python environment, you can just install the dependencies in the virtual environment.
+
+Here is how the directory looks like:
+```
+-- Root
+---- dexenv
+---- IsaacGymEnvs
+---- isaacgym
+```
+
+```
+# download packages
+git clone [email protected]:Improbable-AI/dexenv.git
+git clone https://github.com/NVIDIA-Omniverse/IsaacGymEnvs.git
+
+# download IsaacGym from: 
+# (https://developer.nvidia.com/isaac-gym)
+# unzip it in the current directory
+
+# remove the package dependencies in the setup.py in isaacgym/python and IsaacGymEnvs/
+```
+
+#### Download the assets
+
+Download the robot and object assets from [here](https://huggingface.co/datasets/taochenshh/dexenv/blob/main/assets.zip), and unzip it to `dexenv/dexenv/`.
+
+#### Download the pretrained models
+
+Download the pretrained checkpoints from [here](https://huggingface.co/datasets/taochenshh/dexenv/blob/main/pretrained.zip), and unzip it to `dexenv/dexenv/`.
+
+#### Prepare the docker image
+1. You can download a pre-built docker image:
+```
+docker pull improbableailab/dexenv:latest
+```
+2. Or you can build the docker image locally:
+```
+cd dexenv/docker
+python docker_build.py -f Dockerfile
+```
+
+#### Launch the docker image
+
+To run the docker image, you would need to have the nvidia-docker installed. Follow the instructions [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
+```bash
+# launch docker
+./run_image.sh # you would need to have wandb installed in the python environment
+```
+
+In another terminal
+```bash
+./visualize_access.sh
+# after this, you can close it, just need to run this once after every machine reboot
+```
+
+
+## :scroll: Usage
+
+#### :bulb: Training Teacher
+
+```bash
+# if you are running in the docker, you might need to run the following line
+git config --global --add safe.directory /workspace/dexenv
+
+# debug teacher (run debug first to make sure everything runs)
+cd /workspace/dexenv/dexenv/train/teacher
+python mlp.py -cn=debug_dclaw # show the GUI
+python mlp.py task.headless=True -cn=debug_dclaw # in headless mode
+
+# if you wanna just train the hand to reorient a cube, add `task.env.name=DClawBase`
+python mlp.py task.env.name=DClawBase -cn=debug_dclaw 
+
+# training teacher
+cd /workspace/dexenv/dexenv/train/teacher
+python mlp.py -cn=dclaw
+python mlp.py task.task.randomize=False -cn=dclaw # turn off domain randomization
+python mlp.py task.env.name=DClawBase task.task.randomize=False -cn=dclaw # reorient a cube without domain randomization
+
+# if you wanna change the number of objects or the number of environments
+python mlp.py alg.num_envs=4000 task.obj.num_objs=10 -cn=dclaw
+
+# testing teacher
+cd /workspace/dexenv/dexenv/train/teacher
+python mlp.py alg.num_envs=20 resume_id=<wandb exp ID> -cn=test_dclaw
+# e.g. python mlp.py alg.num_envs=20 resume_id=dexenv/1d1tvd0b -cn=test_dclaw
+
+```
+
+#### :high_brightness: Training Student with Synthetic Point Cloud (student stage 1)
+
+```
+# debug student
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py -cn=debug_dclaw_fptd
+# by default, the command above used the pretrained teacher model you downloaded above, 
+#if you wanna use another teacher model, add `alg.expert_path=<path>`
+python rnn.py alg.expert_path=<path to teacher model> -cn=debug_dclaw_fptd
+
+# training student
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py -cn=dclaw_fptd
+
+# testing student
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py resume_id=<wandb exp ID> -cn=test_dclaw_fptd
+```
+
+#### :tada: Training Student with rendered Point Cloud (student stage 2)
+
+```
+# debug student
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py -cn=debug_dclaw_rptd
+
+# training student
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py -cn=dclaw_rptd
+
+# testing student
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py resume_id=<wandb exp ID> -cn=test_dclaw_rptd
+```
+
+## :rocket: Pre-trained models
+
+We provide the pre-trained models for both the teacher and the student (stage 2) in `dexenv/expert/artifacts`. The models were trained using Isaac Gym preview 3.
+
+```
+# to see the teacher pretrained model
+cd /workspace/dexenv/dexenv/train/teacher
+python demo.py
+
+# to see the student pretrained model
+cd /workspace/dexenv/dexenv/train/student
+python rnn.py alg.num_envs=20 task.obj.num_objs=10  alg.pretrain_model=/workspace/dexenv/dexenv/pretrained/artifacts/student/train-model.pt test_pretrain=True test_num=3 -cn=debug_dclaw_rptd
+```
+