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What is PyCRAM

PyCRAM is the Python 3 re-implementation of CRAM. PyCRAM is a toolbox for designing, implementing and deploying software on autonomous robots. The framework provides various tools and libraries for aiding in robot software development as well as geometric reasoning and fast simulation mechanisms to develop cognition-enabled control programs that achieve high levels of robot autonomy.

PyCRAM is developed in Python with support for the ROS middleware which is used for communication with different software components as well as the robot.

This framework is tested with Ubuntu 20.04, ROS Noetic and Python 3.8

Simple Demonstartion

PyCRAM allows the execution of the same high-level plan on different robot platforms. Below you can see an example of this where the plan is executed on the PR2 and the IAIs Boxy.

Boxy PR2
image alt

The plan that both robots execute is a relativly simple pick and place plan:

  • They start at the world origin
  • park their arms
  • move to the counter
  • observe the object
  • pickup the object
  • move to the kitchen island
  • place the object
  • move to the world origin

The code for this plan can be seen below.

from pycram.worlds.bullet_world import BulletWorld
from pycram.world_concepts.world_object import Object
from pycram.process_module import simulated_robot
from pycram.designators.motion_designator import *
from pycram.designators.location_designator import *
from pycram.designators.action_designator import *
from pycram.designators.object_designator import *
from pycram.datastructures.enums import ObjectType, Arms, Grasp, WorldMode

world = BulletWorld(WorldMode.GUI)
kitchen = Object("kitchen", ObjectType.ENVIRONMENT, "kitchen.urdf")
robot = Object("pr2", ObjectType.ROBOT, "pr2.urdf")
cereal = Object("cereal", ObjectType.BREAKFAST_CEREAL, "breakfast_cereal.stl", pose=Pose([1.4, 1, 0.95]))

cereal_desig = ObjectDesignatorDescription(names=["cereal"])
kitchen_desig = ObjectDesignatorDescription(names=["kitchen"])
robot_desig = ObjectDesignatorDescription(names=["pr2"]).resolve()

with simulated_robot:
    ParkArmsAction([Arms.BOTH]).resolve().perform()

    MoveTorsoAction([TorsoState.HIGH]).resolve().perform()

    pickup_pose = CostmapLocation(target=cereal_desig.resolve(), reachable_for=robot_desig).resolve()
    pickup_arm = pickup_pose.reachable_arms[0]

    NavigateAction(target_locations=[pickup_pose.pose]).resolve().perform()

    PickUpAction(object_designator_description=cereal_desig, arms=[pickup_arm], grasps=[Grasp.FRONT]).resolve().perform()

    ParkArmsAction([Arms.BOTH]).resolve().perform()

    place_island = SemanticCostmapLocation("kitchen_island_surface", kitchen_desig.resolve(), cereal_desig.resolve()).resolve()

    place_stand = CostmapLocation(place_island.pose, reachable_for=robot_desig, reachable_arm=pickup_arm).resolve()

    NavigateAction(target_locations=[place_stand.pose]).resolve().perform()

    PlaceAction(cereal_desig, target_locations=[place_island.pose], arms=[pickup_arm]).resolve().perform()

    ParkArmsAction([Arms.BOTH]).resolve().perform()

world.exit()

Installation

For information on installing PyCRAM please check the guid here.

Documentation

The latest version of the documentation is hosted on Read the Docs here.

The documentation can be found in the doc folder, for instructions on how to build and view the documentation please take a look at the respective README file.

Examples

Examples of features can be found either in the documentation under the 'Examples' Section or in the examples folder. The examples in the examples folder are Jupyter Notebooks which can be viewed and executed, for more information how to do that take a look at the respective README file.

Troubleshooting

If you encounter some error please first take a look at the troubleshooting section and see if the error is mentioned there.

Virtual Building

Within our virtual building, you can find a variety of labs and examples that showcase the use of PyCRAM. These resources are available at our Labs page. They are designed to help you understand and experiment with PyCRAM's capabilities.

Setting Up Your Own Lab

If you're looking to set up your own lab within the virtual building, please refer to the vrb branch of this repository. It includes detailed instructions and templates to guide you through the process.