Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

All code and data to reproduce results in Section 5 of Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation [ICML 2021]

Getting Started

All code developed in Ubuntu 18.04.2 with WSL and Python 3.6.7.

Optional, but recommended. Setup a virtual environment (e.g. VirtualEnv.)

Install Dependencies:

pip install -r requirements.txt

Human Navigation Turing Test (HNTT)

To reproduce our analysis of responses to the HNTT included in Section 5.1 of the ICML paper:

Download HNTT_data.csv from this link (30KiB)

Step through HNTT_data_analysis.ipynb notebook

HNTT Survey Templates and Videos

The HNTT survey templates, with the answer key embedded, can be downloaded from this link (301KiB)

The corresponding HNTT videos can be downloaded from this link (134MiB)

Automated Navigation Turing Test (ANTT)

Training ANTT Models (Section 3.3)

To train ANTT models, download the training dataset from this link (1.95 GiB) then run:

python train.py --model-type ['visuals', 'symbolic', 'topdown', 'barcode'] --human-train <path> --human-test <path> --agent-train <path> --agent-test <path>

Alternatively, to run a hyperparameter sweep with 5-fold cross validation, first update hyperparameters.json, then run:

python cross_validation.py --model-type ['visuals', 'symbolic', 'topdown', 'barcode'] --human-dirs <path(s)> --agent-dirs <path(s)>

To see all the parameters along with their default values, run python cross_validation.py --help.

To monitor training runs:

tensorboard --logdir ./logs/

To plot learning curves with variance (e.g. to reproduce figure 2 in the paper):

python plot_ANTT_training.py

[Optional] To reproduce the barcode or topdown data from the raw trajectories, run:

python topdown/create_topdown_img.py --folders <path(s)> --outdir <path>
python barcodes/create_barcodes.py --indir <path> --outdir <path>

Evaluation (Section 5.2)

To reproduce ANTT analysis included in Section 5.2 of the ICML paper:

Download HNTT_data.csv from this link (30KiB)

Download evaluation dataset from this link (264MiB)

Then either:

• Download trained models (.pt files) and saved model output (.pkl) from this link (9GiB)
• Train your own ANTT models as described above

To evaluate a trained model:

python evaluate_ANTT_model.py --path-to-models PATH --model-type ['BARCODE', 'CNN', 'SYMBOLIC', 'TOPDOWN']

If model is a recurrent CNN or SYMBOLIC model, also pass --subsequence-length N

If model has been previously evaluated, its output for each question in the behavioural study will be saved in a .pkl file. For faster re-evaluation (without classifying replays again) add --load-model-output

To reproduce Figures 9 and 10, plot the evaluation of all ANTT models by:

python plot_ANTT_evaluation.py

Passing the Navigation Turing Test

In later work on "How Humans Perceive Human-like Behavior in Video Game Navigation" published at CHI 2022 we presented the first agent to pass the Navigation Turing Test. Videos of the CHI 2022 agent are available here.

License

Code is licensed under MIT, data and all other content is licensed under Microsoft Research License Agreement (MSR-LA). See LICENSE folder.