Repository for the paper M. Racca, A. Oulasvirta and V. Kyrki, Teacher-Aware Active Robot Learning, HRI 2019 (preprint available here).
Repo structure:
- caal: python package containing the core code behind the different active learners
- catkin_ws: catkin workspace containing all the ROS related components: nao_interface, learner (GUI used by the subject in the experiment and interface to the learners in caal), control_gui (GUI for the experimenter), support (useful launch files)
- dataset: contains AwA2 list of attributes and entities + ground truth (no photos)
- notebooks: contains Jupyter notebooks for the simulation
- ROS (I use Kinetic but I don't see why it should not work with Indigo or Melodic)
- Python (2.7 as it is the one supported by ROS; non-ROS code runs also on Python 3)
- NAO SDK: NAOqi 2.1.2 (see NAOqi_guide)
- Python Packages: NLTK, numpy, scipy, anytree, bidict
- PyQT 5 (for the experiment GUI)
- run the Jupyter notebook
CategoryAttributeLearning.ipynb.
- first, compile the catkin workspace with
caktin_make(catkin_make should also docatkin_init_workspacefor you) and sourced itsource devel/setup.bash - add the caal folder to your
$PYTHONPATH, withexport PYTHONPATH=/path_to_caal_folder/caal:$PYTHONPATH - inside the catkin workspace, under support/launch there are a couple of launch files. The experiment.launch is for the experiment as reported in the paper
- the launch file loads various ROS parameters (where to log, where to find the dataset, time budget), including the IP of your NAO. These have to be changed according to your liking and setup
- start with
roslaunch support experiment.launch - the launch file opens connection with NAO and spawns the 2 GUIs (one for the subject, one for experimenter). The two GUIs were displayed on two different screens during the experiment
Everything authored by me is released under the GNU GPL 3.0 license.