Task models for human robot collaboration
Tools to manipulate and use task models for human robot collaboration.
If you are using this software and or one of its components, we warmly recommend you to cite the following paper:
|[Roncone2017]||(1, 2) Roncone Alessandro, Mangin Olivier, Scassellati Brian Transparent Role Assignment and Task Allocation in Human Robot Collaboration IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore. [PDF] [BIB]|
The top-level directories contain the following code:
tests: unittests (run
python -m unittest discover tests),
visualization: task models and policies visualizations based on d3.js <https://d3js.org/>.
The code from the
task_models package contains a set of classes to
represents models of tasks for human robot collaboration and in particular
hierarchical task models. The code mostly consists in the following components:
action.pymodules define useful classes used in
task.py. In addition to providing useful objects to represent hierarchies of task (HTM), the latter also implements the techniques for extracting such structure that were introduced in [Hayes2016].
libdirectory provides: -
pomdp.py: a python wrapper to Anthony Cassandra's POMDP solver. Please visit pomdp.org. -
pomcp.py: a partial implementation of [Silver2010], -
belief.py: belief representations, -
py23.py: compatibility code for python 2 and 3, -
utils.py: additional helpers.
utilsis a clone of https://github.com/omangin/python-utils.
task_to_pomdp.py: code mostly used for [Roncone2017].
|[Silver2010]||Silver, David and Veness, Joel Monte-Carlo Planning in Large POMDPs (2010)|
|[Hayes2016]||Hayes, Bradley and Scassellati, Brian Autonomously constructing hierarchical task networks for planning and human-robot collaboration, IEEE International Conference on Robotics and Automation (ICRA 2016)|
Prerequisites for using the POMDP solvers
This package requires a binary from Anthony Cassandra's POMDP solver. Please visit pomdp.org for any matter related to the POMDP solver. In order to be using the simplex finite grid method, a fork of the version from cmansley needs to be installed that contains a fix to the original code. You can get the fork here.
The python code is looking for the
pomdp-solve executable in your
$PATH. Here are some instructions on how to compile and install the solver properly (assuming that
~/src is the directory in which you usually place your code):
cd ~/src git clone https://github.com/scazlab/pomdp-solve cd pomdp-solve mkdir build cd build/ ../configure --prefix=$HOME/.local make make install
Make sure that
~/.local/bin is in yout path and now you should have
pomdp-solve installed in it, and it should be available for the python package to be used.
To generate the policy from the experiment in [Roncone2017], please use the script
samples/icra_scenario2pomdp.py. The script will generate the corresponding POMDP model, solve it with Anthony Cassandra's POMDP solver, and store the corresponding policy under
visualization/policy/json/icra.json. To run the full experiment on the baxter robot, please refer to github.com/ScazLab/baxter_collaboration.