Merge pull request #226 from AbdelrhmanBassiouny/giskard_fallschool_c…

…hanges

Giskard fallschool changes

config/multiverse_conf.py

@@ -70,3 +70,6 @@ class MultiverseConfig(WorldConfig):
 
     position_tolerance = 2e-2
     prismatic_joint_position_tolerance = 2e-2
+
+    use_giskard_monitor = False
+    allow_gripper_collision = False

config/world_conf.py

@@ -88,6 +88,17 @@ class WorldConfig:
     """
     Whether to raise an error if the goals are not achieved.
     """
+
+    use_giskard_monitor: bool = True
+    """
+    Whether to use the Giskard goal monitor when executing the goals.
+    """
+
+    allow_gripper_collision: bool = True
+    """
+    Whether to allow the gripper to collide with the objects when planning for the goals.
+    """
+
     @classmethod
     def get_pose_tolerance(cls) -> Tuple[float, float]:
         return cls.position_tolerance, cls.orientation_tolerance

doc/source/orm.md

@@ -1 +1,28 @@
-# ORM
+# ORM
+
+When adding parameters to the Actions/Motions, you need to add those in the ORM classes as well.
+Make sure to set "init=False" and "default=default_value" in the mapped_column if the added parameter is not a required
+parameter.
+
+Example:
+
+```python
+class Action():
+
+    new_parameter: float = 0.0
+
+class ActionORM(Base):
+
+    new_parameter: Mapped[float] = mapped_column(init=False, default=0.0)
+```
+
+When modifying an action by adding another action/motion as part of it make sure to modify the position_results assertion
+in test_node in the test_orm.py and test_simulated_tree, test_tree_creation in test_task_tree.py accordingly. A helpful tecnique is to print the task_tree and count the poses,
+so each action has at least one pose since it has a robot_state unlike motions, then you add the poses that are needed
+in that action:
+```python
+root = pycram.tasktree.task_tree.root
+print(RenderTree(root, style=AsciiStyle()))
+```
+
+Or you could just print the len(position_results) and use that number to modify the assertion.

examples/cram_plan_tutorial.md

@@ -152,11 +152,12 @@ def plan(obj, obj_desig, torso=0.2, place="countertop"):
         location = CostmapLocation(target=obj_desig, reachable_for=robot_desig)
         pose = location.resolve()
         print()
-        NavigateActionPerformable(pose.pose).perform()
+        NavigateActionPerformable(pose.pose, True).perform()
         ParkArmsActionPerformable(Arms.BOTH).perform()
         good_torsos.append(torso)
         picked_up_arm = pose.reachable_arms[0]
-        PickUpActionPerformable(object_designator=obj_desig, arm=pose.reachable_arms[0], grasp=Grasp.FRONT).perform()
+        PickUpActionPerformable(object_designator=obj_desig, arm=pose.reachable_arms[0], grasp=Grasp.FRONT,
+                                prepose_distance=0.03).perform()
 
         ParkArmsActionPerformable(Arms.BOTH).perform()
         scm = SemanticCostmapLocation(place, apartment_desig, obj_desig)
@@ -171,7 +172,7 @@ def plan(obj, obj_desig, torso=0.2, place="countertop"):
                                          reachable_arm=picked_up_arm)
         pose = place_location.resolve()
 
-        NavigateActionPerformable(pose.pose).perform()
+        NavigateActionPerformable(pose.pose, True).perform()
 
         PlaceActionPerformable(object_designator=obj_desig, target_location=pose_island.pose,
                                arm=picked_up_arm).perform()

scripts/test_notebook_examples.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 source /opt/ros/noetic/setup.bash
-source ~/catkin_ws/devel/setup.bash
+source ~/cram_ws/devel/setup.bash
 roscd pycram/examples
 rm -rf tmp
 mkdir tmp

src/pycram/datastructures/enums.py

@@ -216,6 +216,16 @@ class MJCFJointType(Enum):
     FIXED = "fixed"  # Added for compatibility with PyCRAM, but not a real joint type in MuJoCo.
 
 
+class MovementType(Enum):
+    """
+    Enum for the different movement types of the robot.
+    """
+    STRAIGHT_TRANSLATION = auto()
+    STRAIGHT_CARTESIAN = auto()
+    TRANSLATION = auto()
+    CARTESIAN = auto()
+
+
 class MultiverseAPIName(Enum):
     """
     Enum for the different APIs of the Multiverse.