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Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

This repository contains the code for the paper submitted to ICRA 2023.

Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

Jasmine Jerry Aloor*, Jay Patrikar*, Parv Kapoor, Jean Oh and Sebastian Scherer.

*equal contribution

Brief Overview [Video]

Figure Overview

Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies is a critical requirement to enable the real-world deployment of AI agents. Recently Signal Temporal Logic (STL) has been shown to be an effective language for encoding rules as spatio-temporal constraints. This work uses Monte Carlo Tree Search (MCTS) as a means of integrating STL specification into a vanilla LfD policy to improve constraint satisfaction. We propose augmenting the MCTS heuristic with STL robustness values to bias the tree search towards branches with higher constraint satisfaction. While the domain-independent method can be applied to integrate STL rules online into any pre-trained LfD algorithm, we choose goal-conditioned Generative Adversarial Imitation Learning as the offline LfD policy. We apply the proposed method to the domain of planning trajectories for General Aviation aircraft around a non-towered airfield. Results using the simulator trained on real-world data showcase 60% improved performance over baseline LfD methods that do not use STL heuristics.

Installation

Environment Setup

First, we'll create a conda environment to hold the dependencies.

conda create --name stlmcts --file requirements.txt
conda activate stlmcts

Data Setup

The network uses TrajAir Dataset.

cd dataset
wget https://kilthub.cmu.edu/ndownloader/articles/14866251/versions/1

Unzip dataset files in place as shown in the folder tree below.

mcts-stl-planning/
├─ dataset/
│  ├─ 111_days/
│  │  ├─ processed_data/
│  │  │  ├─ test/
│  │  │  ├─ train/
├─ episodes/
├─ gym/
├─ images/
├─ mcts/
├─ model/
├─ rtamt/
├─ saved_models/
├─ costmap.py
├─ play.py

MCTS Parameters

The MCTS is implemented as a recursive function where each iteration ends with a new leaf that corresponds to an action in the trajectory library. For running the algorithm, we can choose any dataset. For example, to test with Behavior Cloning algorithm (BC) with 111_days use:

python play.py --dataset_name 111_days --algo BC --tcn_channel_size 256

To test with goalGAIL algorithm (goalGAIL) with 111_days use:

python play.py --dataset_name 111_days --algo GAIL --tcn_channel_size 512

  • --checkpoint Argument to set checkpoint for MCTS (default = /episodes/)

  • --load_episodes Argument to load episodes (default = False)

  • --algo Baseline algorithm for network (default = BC)

  • --numMCTS Number of MCTS Trees (default = 50)

  • --cpuct Argument to balance the exploration and exploitation (default = 1)

  • --huct Weight of heuristic (default = 4000)

  • --parallel Argument to set parallel execution (default = False)

  • --num_process Number of processes (default = 1000)

  • --algo Baseline algorithm for network (default = BC)

  • --numEpisodeSteps Number of steps in an episode (default = 30)

  • --maxlenOfQueue Maximim length of queue (default = 25600)

  • --numEps Maximum number of episodes (default = 100)

  • --numEpsTest Maximum number of episodes during testing (default = 100)

  • --numIters Number of iterations (default = 1) !!review

  • --plot To plot the trees (default = False)

Inititalization Network

Model Training

For training data we can choose between the 4 training subsets of data labelled 7days1, 7days1, 7days1, 7days1 or the entire dataset 111_days. For example, to train with 7days1 use:

python train.py --dataset_name 7days1

Training will use GPUs if available.

Optional arguments can be given as following:

  • --dataset_folder sets the working directory for data. Default is current working directory (default = /dataset/).

  • --dataset_name sets the data block to use (default = 111_days).

  • --models_folder sets the directory for saved model. Default is saved_models directory (default = /saved_models/).

  • --model_weights sets the model weight to be used (default = model_111_days_4.pt). !! Needs review

  • --obs observation length (default = 11).

  • --preds prediction length (default = 120).

  • --preds_step prediction steps (default = 5).

  • --delim Delimiter used in data (default = ).

  • --use_trajair Option to use trajairnet model(default = False). !! needs review

  • --algo Baseline algorithm for network (default = BC)

  • --total_epochs Total number passes over the entire training data set (default = 10).

  • --model_pth Path to save the models (default = /saved_models/).

TCN Network Arguments

  • --input_channels The number of input channels (x,y,z) (default = 3).
  • --tcn_kernels The size of the kernel to use in each convolutional layer (default = 4).
  • --tcn_channel_size The number of hidden units to use (default = 512).
  • --tcn_layers The number of layers to use. (default = 2)
  • --mlp_layer The number of hidden units in the MLP decoder (default, BC = 91, goalGAIL = ).

Model Testing

  • --dataset_folder sets the working directory for data. Default is current working directory (default = /dataset/).
  • --dataset_name sets the data block to use (default = 7days1).
  • --obs observation length (default = 11).
  • --preds prediction length (default = 120).
  • --preds_step prediction steps (default = 10).
  • --delim Delimiter used in data (default = ).
  • --model_dir Path to load the models (default = /saved_models/).
  • --epoch Epoch to load the model.

STL Library: RTAMT

To install the RTAMT library for monitoring of Signal Temporal Logic (STL) rtamt follow the package's installation procedure

Additionally, if the antlr4 dependency throws an error, follow the conda installation here

TrajAir Dataset

More information about TrajAir dataset is avaiable at link.

Cite

If you have any questions, please contact [email protected] or open an issue on this repo.

If you find this repository useful for your research, please cite the following paper:

@misc{https://doi.org/10.48550/arxiv.2209.13737,
  doi = {10.48550/ARXIV.2209.13737},
  
  url = {https://arxiv.org/abs/2209.13737},
  
  author = {Aloor, Jasmine Jerry and Patrikar, Jay and Kapoor, Parv and Oh, Jean and Scherer, Sebastian},
  
  keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}