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
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 |
|---|---|
 |
 |
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.bullet_world import BulletWorld, 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.enums import ObjectType
world = BulletWorld()
kitchen = Object("kitchen", ObjectType.ENVIRONMENT, "kitchen.urdf")
robot = Object("pr2", ObjectType.ROBOT, "pr2.urdf")
cereal = Object("cereal", ObjectType.BREAKFAST_CEREAL, "breakfast_cereal.stl", position=[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([0.3]).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=["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()
For information on installing PyCRAM please check the guid here.
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 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.
If you encounter some error please first take a look at the troubleshooting section and see if the error is mentioned there.
