Use the following script to setup a virtual environment and cache shortest paths and particle measurements.
source install.sh
Use the provided train.py script to train the baseline seq2seq model. The script takes the following options:
| Option | Possible values |
|---|---|
feedback |
'sample', 'teacher' |
eval_type |
'val', 'test' |
blind |
'vision', 'language', '' |
Note that in the traditional experiment where no blinding occurs, the blind parameter is blank. As an example, to train a model with teacher forcing, use the following command.
python train.py --feedback=teacher --eval_type=val --lr=0.0001
Note that your device must be CUDA-enabled.
To make videos of our trials using the simulator run the following:
python make_simulation_videos.py
Download videos of robotslang trials here
Contact us at [email protected]