The Transfer of Human Trust in Robot Capabilities across Tasks

This is the supplementary material for the paper The Transfer of Human Trust in Robot Capabilities across Tasks, Robotics Science and Systems (RSS) 2018.

Requirements

To run the code, you will need:

• python3
• pytorch v0.3.0.post4 (Note: v0.4.0 appears to introduce some breaking changes)
• spacy (for the word vectors)
• numpy
• scipy
• sklearn

The testing machine used the Anaconda distribution. To run the experiments automatically using the startAllExps.sh script, you will need unix screen.

Folders and Files

• code contains all the code used to run the experiments and analyze the results.
• data contains the data collected during our experiments and the word embeddings.
• docs contains supplementary information (e.g., experimental setup details) that didn't make it to the paper due to space constraints.

Quickstart Q&A

How can I replicate the plots in the paper?

The easiest way is to run though the ModeComparison.ipynb jupyter notebook. This will make use of the our saved results (in the code/results directory).

How do I re-run Experiments E1 and E2 in the paper?

If you want to re-run everything, you can run the startAllExps.sh script file. This uses screen to start a whole batch of parallel runs. This will take a while, so go have some coffee/sleep. Results will be saved to the code/results directory. Saved models will be in code/savedmodels. If you want to re-run specific experiments (with certain algorithm/dataset combinations), take a look at the individual commands in the startAllExps.sh script and the runExperiment.py pythonfile. The runExperiment.py file is relatively easy to use.

./runExperiment.py [household|driving] [experiment type] [alg] [alg options] [latent task dimensionality]

As an example, to run the constant-mean GP with a latent task space of 3 dimensions (test on held out participants), run

./runExperiment.py household 3participant gp 0 3

Where can I find the models described in the paper?

The models are in trustmodels.py in separate classes. The neural model is called NeuralTrustNet and the GP is called GPTrustTransfer. The different GP variants are controlled via the switches. To use the prior mean function, set the usepriormean parameter to True. To use pseudo-observations, set usepriorpoints to True. You can actually use both, but this didn't seem to give strictly better results in our initial trials.