/forgg

Algorithms for asympotically optimal task and motion planning

Primary LanguagePythonOtherNOASSERTION

metis

This package is contains tools for evaluating models and inference algorithms for task and motion planning. The name comes from Metis, a Greek goddess of wisdom, mother of Athena.

Maintainer

Dependencies

  • numpy
  • scipy
  • shapely
  • pybox2d
  • triangle
  • (optional) pydot
  • (optional) nosetests

Everything can be installed through pip.

pip install numpy scipy shapely Box2D triangle

If you are not in a virtualenv, this will install to /usr/ by default, and will require sudo privileges. If you'd rather install somewhere else, run

pip install --upgrade --install-option="--prefix=$MY_PREFIX" numpy scipy shapely Box2D triangle

with $MY_PREFIX set to, for instance, $HOME/rrg. Note that you must also update your PYTHONPATH environment variable accordingly to use the packages.

NOTE: installing the pybox2d from pip might fail if swig is not installed. If that happens, apt-get install swig may help.

Installation

This will provide minimal output. To run the tests and see the normal nose output, from the package directory run nosetests without arguments.

Usage

A simple block-pushing example is included; more comprehensive and extensible examples are planned for short term development in the future. This example world has a robot pushing objects between three rooms. To run the example, you must create a yaml description of the problem to solve. Examples of the format can be found in the cfg directory. Briefly, a valid job file specifies the start configuration of the world, as well as algorithmic parameters. For example:

noupper_blocked_forgg_0200_00: # name for this job
  algorithm: forgg             # the algorithm to use ('forgg' or 'tamp')
  domain:                      # Parameters affecting the problem domain
    upper_door: True           # True if there should be two doors between the
                               # rooms; False if there should be only one.
  instance:                    # Parameters affecting the problem instance
    box1:                      # The initial location of the first object
    - 3                        #  x coordinate (in meters)
    - 5                        #  y coordinate (in meters)
    - -0.2                     #  orientation (in radians)
    box2:                      # The initial location of the second object
    - 5
    - 2.5
    - 0.1
    robot:                     # The initial location of the robot
    - 2
    - 2
    - 0
  solver:                      # Solver parameters
    count: 200                 # Number of samples to generate in each
                               # mode factor
    seed: 0                    # PRNG seed value

To run this example, call

./run.py cfg/forgg.yaml

Command line output is sent to a log file by default, which you can view in real time with

tail -f noupper_blocked_forgg_0200_00.out

To solve the example problem with count=200, the planner must explre around 240000 vertices. On a typical desktop, the planner can explore around 200-400 vertices per second, for a total planning time of 10-20 minutes.