STATE-NAV is a learning-based traversability estimation and risk-sensitive velocity-based navigation framework for bipedal robots operating in diverse and uneven environments. It learns a stability-aware representation of terrain by carefully selecting a self-supervised locomotion signal and integrates this knowledge into a hierarchical planning framework for safe and efficient navigation.

• Avoids brittle terrain heuristics: No reliance on hand-tuned cost functions that break in new environments
• Robot-specific and environment-agnostic: Provides a generalizable way to measure traversability tailored to bipedal locomotion
• Safe learning-planning integration: Offers a clean abstraction that integrates learning and planning without sacrificing safety
• Modular and extensible: Allows researchers to plug in other instability metrics, controllers, or planners

• Traversability Estimation: TravFormer, a transformer-based neural network, predicts bipedal instability along with its uncertainty. Traversability is defined as a stability-aware command velocity: the fastest command that keeps predicted instability within a user-specified limit. The model supports risk-aware planning via Value at Risk (VaR) and operates directly from elevation maps.

• TravRRT Global Planning: A global planner that leverages the predicted stability-aware velocity to generate time-efficient and risk-sensitive paths. Consistently avoids unsafe terrain without manual weight tuning or environment-specific adjustments.

If you use STATE-NAV in your research, please cite:

@article{yoon2025state,
  title={STATE-NAV: Stability-Aware Traversability Estimation for Bipedal Navigation on Rough Terrain},
  author={Yoon, Ziwon and Zhu, Lawrence Y and Gan, Lu and Zhao, Ye},
  journal={arXiv preprint arXiv:2506.01046},
  year={2025}
}

Clone the repo:

cd $(path_to_your_catkin_workspace)/src
git clone 

First, Install Docker: https://docs.docker.com/desktop/install/ubuntu/ Second, install nvidia container toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

An easy way is to download prebuilt container from docker.io/twoleggedcoffeedrinker/state_nav:latest.

Or, you can build by your own from scratch.

cd $(path_to_your_catkin_workspace)/src/state_nav/docker
docker build -f Dockerfile.x64 -t biped_nav_sim .
# You might need to change the image name in `runfrombuild.sh`

After building, open a container.

Caution* Make sure to change the line 3 in your runfrombuild.sh for path to your home directory and catkin ws.

• When reopening container throws an error for XAUTH, delete your /tmp/.X11-unix and /tmp/.docker.xauth and then running ./runfrombuild would work. Running ./runfrombuild with sudo might potentially cause a problem for setting home directory.

sudo rm -rf /tmp/.X11-unix
sudo rm -rf /tmp/.docker.xauth

./runfrombuild.sh

After opening an container, open an interactive shell in the container.

Run the following. You should be in the container's filesystem.

. $HOST_HOME_DIR/$(path_to_your_catkin_workspace)/src/state_nav/docker/startup.sh

This will build ROS package. Now you are good to go. Keep in mind that this build is only valid in this specific container. If you open a new container, you have to do the same thing again. Also, when you do ROS build in a docker container, it might conflict if you do ROS build outside of the container.

Run the followings in separate shells.

(1) Modify executables/configs/planning_config.yaml.

(2) rosrun state_nav main_traversability_estimation.py

# Launches the traversability estimation node that converts elevation map to traversability map.
# Computes stability-aware traversability maps using the TravFormer neural network.
# This node publishes costmaps that indicate safe navigation regions for the bipedal robot.

(3) rosrun state_nav main_global_planning.py

# Launches the global path planning node that uses TravRRT* to compute optimal paths
# based on the traversability maps. This node subscribes to costmaps and robot pose,
# then publishes planned paths for navigation.

(4) rosbag play $(path_to_your_catkin_workspace)/src/state_nav/ROS/1017_2.bag

# Plays back recorded sensor data (camera pointcloud, elevation map, path plan, etc.) from a rosbag file.
# It is recording of one of our outdoor experiments.
# Replace $(path_to_your_catkin_workspace) with your actual catkin workspace path.

(5) rosrun rviz rviz -d $(path_to_your_catkin_workspace)/src/state_nav/ROS/rviz_setting.rviz

# Launches RViz visualization tool with a pre-configured setup to visualize
# the traversability maps, planned paths, robot pose, and other ROS topics.
# Replace $(path_to_your_catkin_workspace) with your actual catkin workspace path.

This project builds upon the following open-source works:

The Docker setup and RViz visualization configurations are adapted from elevation_mapping_cupy (MIT License).

The RRT* (Rapidly-exploring Random Tree Star) implementation is based on pypolo (MIT License), a Python library for Robotic Information Gathering.

We gratefully acknowledge the contributions of these projects to the robotics community.