================ Quickstart Guide ================ This guide will help you get started with PyCRAM quickly and create your first belief state and plan. This guide assumes you have already installed PyCRAM either via pip or from source. If you haven't done so, please refer to the installation instructions in the documentation. ----------------------------- Intro to Semantic Digital Twin ----------------------------- PyCRAM uses the `semantic_digital_twin `__ package to manage the belief state and the semantic information of it. To learn more about what the semantic digital twin is capable of refer to its `documentation `__. The semantic digital twin is a belief state representation, that means it represents the robot's knowledge about itself and its environment according to knowledge present before and sensor inputs that are taken during execution. On a basis a semantic digital twin world is made up of three main components: - Bodies: The physical objects in the world including the robot itself. - Connections: The connection between bodies defining their relative poses and possible movements. - Degree of Freedoms (DOFs): The possible movements of the connections. If you are familiar with URDF files, you can think of bodies as links, connections as joints and DOFs as the possible movements of the joints (like prismatic, revolut, etc.). The bodies and connections build the kinematic structure of the world. In the semantic digital twin everything in the world is connected via connections starting from a root body. This then creates a tree structure that defines how everything is connected. The connections define which bodies are connected but not how they move relative to each other. This is where the DOFs come into play. A connection can have multiple DOFs defining how the child body can move relative to the parent body. --------------------------- Setting Up Your Environment --------------------------- The first step is to set up the environment. This involves loading the robot, environment and object models and configuring them as needed. For this guide we will use the PR2 in an apartment environment as well as a milk bottle. Now to set up the environment, we will use URDF files for the robot and the environment and a STL file for the milk bottle. .. code-block:: python from semantic_digital_twin.adapters.mesh import STLParser from semantic_digital_twin.adapters.urdf import URDFParser apartment_world = URDFParser.from_file("/resources/worlds/apartment.urdf").parse() milk_world = STLParser("/resources/objects/milk.stl").parse() pr2_world = URDFParser.from_file("/resources/robots/pr2_calibrated_with_ft.urdf").parse() As you might have noticed each of the parsers returns an independent world which is not really useful. Therefore we need to merge them into a single world. .. code-block:: python from semantic_digital_twin.world import World world = World() world.merge(apartment_world) world.merge(milk_world) To merge the PR2 robot with the world we need to specify the drive that the robot has since it is used to connect the root body of the robot to the root of the other world. In this case the drive is an OmniDrive. Since we are modifiing the world we need to use the modify_world context manager. .. code-block:: python from semantic_digital_twin.drives.omni_drive import OmniDrive with world.modify_world(): pr2_root = pr2_world.root world_root = world.root drive_connection = OmniDrive.create_with_dofs(parent=world_root, child=pr2_root, world=world) world.merge_world(pr2_world, drive_connection) Now we have a semantic digital twin that contains the apartment, the milk bottle and the PR2 robot. You can visualize it using the VizMarkerPublisher, keep in mind that that uses the ROS visualization markers so you need to have ROS installed and sourced. .. code-block:: python from semantic_digital_twin.adapters.viz_marker_publisher import VizMarkerPublisher import rclpy rclpy.init() node = rclpy.create_node("simple_viz_node") viz_publisher = VizMarkerPublisher(world, node) You can now open RViz2 and add a Marker display subscribing to the topic "/semworld/viz_marker" to see the world. ----------------------- Writing your First Plan ----------------------- Now that we have set up the environment, we can write our first plan. A plan in PyCRAM is a structured sequence of actions which the robot will execute to achieve a specific goal. For more details on how plans work in PyCRAM, refer to the :ref:`_plan_header`. The plan will consist of the following steps: 1. Park the robot's arms. 2. Raise the robot's torso. 3. Move the robot to a position near the milk bottle. 4. Pick up the milk bottle. 5. Move the robot to a position near the table. 6. Place the milk bottle on the table. .. code-block::python from pycram.language import SequentialPlan from pycram.robot_plans.actions import ParkArmsActionDescription, MoveTorsoActionDescription, NavigateActionDescription, PickUpActionDescription, PlaceActionDescription from pycram.datastructures.dataclasses import Context from pycram.datastructures.enums import Arms, TorsoState from pycram.datastructures.pose import PoseStamped from semantic_digital_twin.robots.pr2 import PR2 context = Context(world, PR2.from_world(world)) milk_body = world.get_body_by_name("milk.stl") SequentialPlan(context, ParkArmsActionDescription(Arms.BOTH), MoveTorsoActionDescription(TorsoState.HIGH), NavigateActionDescription(PoseStamped.from_list([2.0, 2.0, 0.0], [0, 0, 0, 1])), PickUpActionDescription(milk, Arms.RIGHT), NavigateActionDescription(PoseStamped.from_list([4.0, 4.0, 0.0], [0, 0, 0, 1])), PlaceActionDescription(milk, PoseStamped.from_list([4.2, 4.0, 1.0], [0, 0, 0, 1])) ) What did we just do here? We first created a context which holds the world as well as the semantic description of the PR2 robot in that world. This context is used by the plan to determine in which world and by which robot the plan should be executed. Next, we retrieved the milk bottle body from the world to use it in the pick-up and place actions. Finally, we created a SequentialPlan, meaning all actions will be executed one after another in the order they are defined. To execute the plan, we need to determine if it should be run in simulation or on a real robot and the call perform. .. code-block:: python from pycram.process_modules import simulated_robot with simulated_robot: plan.perform() Congratulations!🎉 You have just written and executed your first plan in PyCRAM.