Code accompanying "Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction" (Edward Schmerling, Karen Leung, Wolf Vollprecht, Marco Pavone).

Video: http://stanford.edu/~schmrlng/Schmerling.Leung.Vollprecht.Pavone.ICRA18.mp4

Raw dataset: https://github.com/StanfordASL/TrafficWeavingCVAE/releases/download/ICRA18/human_human_traffic_weaving_rosbags.zip

Run setup.py located in the base directory of this repository before running anything else and follow the prompts (default choices in parentheses) in order to download/unzip the dataset linked above, as well as symlink the ROS package traffic_weaving_prediction and Julia package TrafficWeavingPlanner into their correct locations.

• 0. Bag Extraction
  • Data preprocessing: runs immediately with the ROS and Python dependencies listed below.
• 1. CVAE Training.ipynb
  • Model training: runs immediately with the Python dependencies listed below.
• 2. Exploring Predictions.ipynb
  • Visualizing predicted human action sequence distributions: requires an exported model from step 1 and the ROS, Python, and Julia dependencies listed below. Before using this notebook
    roslaunch traffic_weaving_prediction predict.launch model:={exported_model_dir}
    (wraps cvae_node_predict.py). Pretrained models (the results of running steps 0 and 1) may be found in the pretrained_models subdirectory of this repository, e.g., you may use {exported_model_dir} = .../TrafficWeavingCVAE/pretrained_models/slim_features_default_hps.
• 3. Human-in-the-Loop Simulation.ipynb
  • Testing prediction model and policy construction: requires an exported model from step 1 and the ROS, Python, Julia, and VTD dependencies listed below. Before using this notebook
    roslaunch vtd_interface human_in_the_loop.launch
    (handles VTD simulator input/output) and
    roslaunch traffic_weaving_prediction policy.launch model:={exported_model_dir}
    (wraps cvae_node_scoring.py).
• 4. ICRA18 Videos.ipynb
  • Visualizing predictions over the duration of a traffic weaving interaction: requires ROS, Python, and Julia dependencies below, and
    roslaunch traffic_weaving_prediction predict.launch.

• ROS Kinetic
• Python 2.7 (required for learning code, notebooks 0 and 1)
  • TensorFlow 1.3
  • pandas
  • numpy
  • matplotlib
  • sympy
  • json
  • h5py
• Julia 0.6 (required for controls/visualization code, notebooks 2-4)
  • At the Julia prompt,

    Pkg.clone("https://github.com/schmrlng/DifferentialDynamicsModels.jl")
    Pkg.clone("https://github.com/schmrlng/LinearDynamicsModels.jl")
    Pkg.update()

  • git clone https://github.com/StanfordASL/vtd_interface.git into the src directory of your ROS catkin workspace. Even if you are not using VTD to conduct human-in-the-loop trials, the TrafficWeavingPlanner depends on some message types defined in vtd_interface.
• VIRES Virtual Test Drive (VTD, required for human-in-the-loop simulation, notebook 3)
  • Contact VIRES for a driving simulator license.
  • The VTD project defining the highway on-ramp/off-ramp scenario used in this work may be accessed here: http://stanford.edu/~schmrlng/TrafficWeavingVTDProject.zip; we are not sure how portable these projects are so please file an issue if you are having difficulties loading up TrafficWeavingVTDProject.vpj contained within that zip file.