Adaptive Diffusion Terrain Generation

[CoRL 24] Adaptive Diffusion Terrain Generation for Autonomous Uneven Terrain Navigation

Youwei Yu*, Junhong Xu*, Lantao Liu

[paper][arXiv][project page]

Environment Setup

git clone https://github.com/youwyu/Adaptive-Diffusion-Terrain.git

1. Isaac Gym, DDPM, Python3.8-dev (Make sure you have mini/ana conda installed)

. install.sh  ## Make sure using . rather than bash or sh install.sh

2. Semi-Global Matching on GPU

Make sure the CMake version is at least 3.18, otherwise install by Kitware at https://apt.kitware.com or build from source

wget https://github.com/Kitware/CMake/releases/download/v3.31.0/cmake-3.31.0.tar.gz
tar -xvf cmake-3.31.0.tar.gz
cd cmake-3.31.0
./configure
make
sudo make install

Change the CUDA path in contexts/simsense/setup.py Line#35

pip install -e contexts/simsense

Teacher & Student Policy

If you wanna use wandb, change Line#119, #120 in auto_train

python3 auto_train.py

Notes:

1. Terrain context will auto save as json file.
2. Teacher: specify the file to load the checkpoint, o.w. it will train from 0.
3. Student: it will auto find the json, or user specify json path. o.w. the program return 1.
4. We use a single RTX 4090 with 24GB RAM. For smaller RAM, we suggest lower num_agents_per_terrain and num_agents_per_terrain_distill in cfg/base_config. The number can be estimated roughly as YOUR_RAM * 4.
5. If you don't want privileged knowlege and save training time and RAM, set all use_globalmap to False.
6. We plan to release the ROS code soon. However, trained checkpoints will be planned right after the submission of our next work.

Miscell

We understand the code is fully non-optimized as we do not care about simulation training during our bed time. We kindly ask you to cite our work if you leverage the code.

@inproceedings{
   yu2024adaptive,
   title={\href{https://openreview.net/forum?id=xYleTh2QhS}{Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation}},
   author={Youwei Yu and Junhong Xu and Lantao Liu},
   booktitle={8th Annual Conference on Robot Learning},
   year={2024}
}