diff --git a/doc/source/index.rst b/doc/source/index.rst
index fb1161d2d..65293c4a4 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -1,4 +1,4 @@
-==================================
+ ==================================
Welcome to pycram's documentation!
==================================
diff --git a/examples/improving_actions.ipynb b/examples/improving_actions.ipynb
index 802648b9e..7bbfc41ef 100644
--- a/examples/improving_actions.ipynb
+++ b/examples/improving_actions.ipynb
@@ -37,11 +37,13 @@
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='wide_stereo_optical_frame']\n",
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='narrow_stereo_optical_frame']\n",
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='laser_tilt_link']\n",
- "[WARN] [1710335617.063405]: Could not import RoboKudo messages, RoboKudo interface could not be initialized\n"
+ "[WARN] [1711028821.218958]: Could not import RoboKudo messages, RoboKudo interface could not be initialized\n"
]
}
],
"source": [
+ "import json\n",
+ "\n",
"import numpy as np\n",
"import os\n",
"import random\n",
@@ -53,9 +55,11 @@
"\n",
"import plotly\n",
"import plotly.graph_objects as go\n",
+ "import tqdm\n",
"\n",
"from probabilistic_model.learning.jpt.jpt import JPT\n",
"from probabilistic_model.learning.jpt.variables import infer_variables_from_dataframe\n",
+ "from probabilistic_model.probabilistic_circuit.probabilistic_circuit import ProbabilisticCircuit\n",
"from random_events.events import Event\n",
"\n",
"import pycram.orm.base\n",
@@ -69,7 +73,7 @@
"from pycram.ros.viz_marker_publisher import VizMarkerPublisher\n",
"from pycram.process_module import ProcessModule, simulated_robot\n",
"from pycram.resolver.probabilistic.probabilistic_action import MoveAndPickUp\n",
- "from pycram.task import task_tree\n",
+ "from pycram.task import task_tree, reset_tree\n",
"\n",
"ProcessModule.execution_delay = False\n",
"np.random.seed(69)\n",
@@ -78,8 +82,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:13:37.164656Z",
- "start_time": "2024-03-13T13:13:35.538099Z"
+ "end_time": "2024-03-21T13:47:01.262567Z",
+ "start_time": "2024-03-21T13:46:59.567924Z"
}
},
"id": "690c193e24875d2b",
@@ -102,8 +106,8 @@
"metadata": {
"collapsed": true,
"ExecuteTime": {
- "end_time": "2024-03-13T13:13:37.183725Z",
- "start_time": "2024-03-13T13:13:37.165474Z"
+ "end_time": "2024-03-21T13:47:01.280456Z",
+ "start_time": "2024-03-21T13:47:01.263722Z"
}
},
"outputs": [],
@@ -135,8 +139,8 @@
"Unknown tag \"material\" in /robot[@name='plane']/link[@name='planeLink']/collision[1]\n",
"Unknown tag \"contact\" in /robot[@name='plane']/link[@name='planeLink']\n",
"Unknown tag \"material\" in /robot[@name='plane']/link[@name='planeLink']/collision[1]\n",
- "Unknown tag \"contact\" in /robot[@name='plane']/link[@name='planeLink']\n",
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='base_laser_link']\n",
+ "Unknown tag \"contact\" in /robot[@name='plane']/link[@name='planeLink']\n",
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='wide_stereo_optical_frame']\n",
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='narrow_stereo_optical_frame']\n",
"Unknown attribute \"type\" in /robot[@name='pr2']/link[@name='laser_tilt_link']\n",
@@ -157,8 +161,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:13:37.608057Z",
- "start_time": "2024-03-13T13:13:37.184410Z"
+ "end_time": "2024-03-21T13:47:01.710037Z",
+ "start_time": "2024-03-21T13:47:01.281829Z"
}
},
"id": "a66e49b86c6c92f1",
@@ -177,9 +181,16 @@
{
"cell_type": "code",
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ProbabilisticCircuit with 15 nodes and 14 edges\n"
+ ]
+ },
{
"data": {
- "text/html": " \n "
+ "text/html": " \n "
},
"metadata": {},
"output_type": "display_data"
@@ -190,20016 +201,20016 @@
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@@ -21141,7 +21110,7 @@
"plotlyServerURL": "https://plot.ly"
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"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:13:38.569644Z",
- "start_time": "2024-03-13T13:13:37.609252Z"
+ "end_time": "2024-03-21T13:47:02.951463Z",
+ "start_time": "2024-03-21T13:47:01.712089Z"
}
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@@ -21183,58 +21152,32 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 500/500 [00:32<00:00, 15.41it/s, Success Probability=0.336]\n"
+ "100%|██████████| 500/500 [00:30<00:00, 16.24it/s, Success Probability=0.228]\n",
+ "Inserting TaskTree into database: 100%|██████████| 5049/5049 [01:15<00:00, 66.72it/s] \n"
]
}
],
"source": [
+ "pycram.orm.base.ProcessMetaData().description = \"Experimenting with Pick Up Actions\"\n",
"fpa.sample_amount = 500\n",
- "with simulated_robot:\n",
- " fpa.batch_rollout()\n"
+ "batches = 1\n",
+ "for batch in tqdm.tqdm(range(batches), total=batches, desc=\"Processing Batches\", disable=True):\n",
+ " with simulated_robot:\n",
+ " fpa.batch_rollout()\n",
+ " task_tree.insert(session)\n",
+ " reset_tree()\n",
+ "session.commit()"
],
"metadata": {
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"ExecuteTime": {
- "end_time": "2024-03-13T13:14:11.029579Z",
- "start_time": "2024-03-13T13:13:38.570880Z"
+ "end_time": "2024-03-21T13:48:49.425825Z",
+ "start_time": "2024-03-21T13:47:02.952200Z"
}
},
"id": "7461b696b899f603",
"execution_count": 5
},
- {
- "cell_type": "code",
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Inserting TaskTree into database: 100%|██████████| 5299/5299 [01:19<00:00, 66.58it/s] \n"
- ]
- },
- {
- "data": {
- "text/plain": "TaskTreeNode(id=1, action_id=None, action=None, start_time=datetime.datetime(2024, 3, 13, 14, 13, 37, 62434), end_time=None, status=, reason=None, parent_id=None, parent=None, process_metadata_id=1)"
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "pycram.orm.base.ProcessMetaData().description = \"Experimenting with Pick Up Actions\"\n",
- "task_tree.insert(session)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-03-13T13:15:30.631935Z",
- "start_time": "2024-03-13T13:14:11.030250Z"
- }
- },
- "id": "546851521d97359",
- "execution_count": 6
- },
{
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@@ -21250,27 +21193,27 @@
"outputs": [
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- "text/plain": " arm grasp relative_x relative_y\n0 right right -0.034396 0.253821\n1 left right 0.027372 0.346807\n2 right right 0.051392 0.319307\n3 left right 0.074518 0.265504\n4 left left 0.174954 0.857668\n.. ... ... ... ...\n163 right front -0.721319 0.461749\n164 left front -0.407319 -0.462141\n165 right front -0.872285 0.151296\n166 right left -0.371555 0.522857\n167 right right -0.492056 -0.485929\n\n[168 rows x 4 columns]",
- "text/html": "\n\n
\n \n \n | \n arm | \n grasp | \n relative_x | \n relative_y | \n
\n \n \n \n 0 | \n right | \n right | \n -0.034396 | \n 0.253821 | \n
\n \n 1 | \n left | \n right | \n 0.027372 | \n 0.346807 | \n
\n \n 2 | \n right | \n right | \n 0.051392 | \n 0.319307 | \n
\n \n 3 | \n left | \n right | \n 0.074518 | \n 0.265504 | \n
\n \n 4 | \n left | \n left | \n 0.174954 | \n 0.857668 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 163 | \n right | \n front | \n -0.721319 | \n 0.461749 | \n
\n \n 164 | \n left | \n front | \n -0.407319 | \n -0.462141 | \n
\n \n 165 | \n right | \n front | \n -0.872285 | \n 0.151296 | \n
\n \n 166 | \n right | \n left | \n -0.371555 | \n 0.522857 | \n
\n \n 167 | \n right | \n right | \n -0.492056 | \n -0.485929 | \n
\n \n
\n
168 rows × 4 columns
\n
"
+ "text/plain": " arm grasp relative_x relative_y\n0 left left -0.546259 -0.356499\n1 left right -0.203552 -0.605282\n2 left left -0.230694 -0.350203\n3 left front -0.366892 0.442297\n4 left right -0.296431 0.054815\n.. ... ... ... ...\n109 right front -0.014655 0.674613\n110 right right 0.039657 0.299975\n111 left front -0.173449 0.398118\n112 left left -0.155267 0.715950\n113 left right 0.148996 0.282944\n\n[114 rows x 4 columns]",
+ "text/html": "\n\n
\n \n \n | \n arm | \n grasp | \n relative_x | \n relative_y | \n
\n \n \n \n 0 | \n left | \n left | \n -0.546259 | \n -0.356499 | \n
\n \n 1 | \n left | \n right | \n -0.203552 | \n -0.605282 | \n
\n \n 2 | \n left | \n left | \n -0.230694 | \n -0.350203 | \n
\n \n 3 | \n left | \n front | \n -0.366892 | \n 0.442297 | \n
\n \n 4 | \n left | \n right | \n -0.296431 | \n 0.054815 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 109 | \n right | \n front | \n -0.014655 | \n 0.674613 | \n
\n \n 110 | \n right | \n right | \n 0.039657 | \n 0.299975 | \n
\n \n 111 | \n left | \n front | \n -0.173449 | \n 0.398118 | \n
\n \n 112 | \n left | \n left | \n -0.155267 | \n 0.715950 | \n
\n \n 113 | \n left | \n right | \n 0.148996 | \n 0.282944 | \n
\n \n
\n
114 rows × 4 columns
\n
"
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "samples = pd.read_sql_query(fpa.query_for_database(), engine)\n",
+ "samples = pd.read_sql(fpa.query_for_database(), engine)\n",
"samples"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:15:30.648040Z",
- "start_time": "2024-03-13T13:15:30.632757Z"
+ "end_time": "2024-03-21T13:48:49.442672Z",
+ "start_time": "2024-03-21T13:48:49.426520Z"
}
},
"id": "5d9cd8fe3195d669",
- "execution_count": 7
+ "execution_count": 6
},
{
"cell_type": "markdown",
@@ -21289,18 +21232,33 @@
"variables = infer_variables_from_dataframe(samples, scale_continuous_types=False)\n",
"model = JPT(variables, min_samples_leaf=25)\n",
"model.fit(samples)\n",
- "model = model.probabilistic_circuit\n",
- "arm, grasp, relative_x, relative_y = model.variables"
+ "model = model.probabilistic_circuit"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:17:05.078605Z",
- "start_time": "2024-03-13T13:17:05.071510Z"
+ "end_time": "2024-03-21T13:48:49.458656Z",
+ "start_time": "2024-03-21T13:48:49.443434Z"
}
},
"id": "8cfff78b3abd589b",
- "execution_count": 21
+ "execution_count": 7
+ },
+ {
+ "cell_type": "code",
+ "outputs": [],
+ "source": [
+ "arm, grasp, relative_x, relative_y = model.variables"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-03-21T13:48:49.462221Z",
+ "start_time": "2024-03-21T13:48:49.459897Z"
+ }
+ },
+ "id": "77642e8f22ac880a",
+ "execution_count": 8
},
{
"cell_type": "markdown",
@@ -21321,20016 +21279,20016 @@
"data": [
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"plotlyServerURL": "https://plot.ly"
}
},
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+ "text/html": ""
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"metadata": {},
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@@ -42240,12 +42184,12 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:17:08.248386Z",
- "start_time": "2024-03-13T13:17:07.484957Z"
+ "end_time": "2024-03-21T13:48:50.379158Z",
+ "start_time": "2024-03-21T13:48:49.463290Z"
}
},
"id": "825dceb35326faf7",
- "execution_count": 22
+ "execution_count": 9
},
{
"cell_type": "markdown",
@@ -42264,7 +42208,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 100/100 [00:15<00:00, 6.66it/s, Success Probability=0.53]\n"
+ "100%|██████████| 100/100 [00:06<00:00, 14.76it/s, Success Probability=0.31]\n"
]
}
],
@@ -42277,12 +42221,12 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:17:25.859550Z",
- "start_time": "2024-03-13T13:17:10.842375Z"
+ "end_time": "2024-03-21T13:48:57.163741Z",
+ "start_time": "2024-03-21T13:48:50.380442Z"
}
},
"id": "6bfc00e5d93a19af",
- "execution_count": 23
+ "execution_count": 10
},
{
"cell_type": "markdown",
@@ -42318,30 +42262,21 @@
],
"source": [
"kitchen = Object(\"kitchen\", ObjectType.ENVIRONMENT, \"apartment.urdf\")\n",
+ "\n",
"milk.set_pose(Pose([0.5, 3.15, 1.04]))\n",
"milk_description = ObjectDesignatorDescription(types=[ObjectType.MILK]).ground()\n",
"fpa = MoveAndPickUp(milk_description, arms=[Arms.LEFT.value, Arms.RIGHT.value, Arms.BOTH.value], grasps=[Grasp.FRONT.value, Grasp.LEFT.value, Grasp.RIGHT.value, Grasp.TOP.value], policy=model)\n",
- "fpa.sample_amount = 200"
+ "fpa.sample_amount = 200\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:17:30.641037Z",
- "start_time": "2024-03-13T13:17:28.025570Z"
+ "end_time": "2024-03-21T13:48:59.559168Z",
+ "start_time": "2024-03-21T13:48:57.164442Z"
}
},
"id": "90d7cb5e1e1cc7fd",
- "execution_count": 24
- },
- {
- "cell_type": "markdown",
- "source": [
- "Let's look at the density of the relative x and y position of the robot with respect to the milk. We can see that he would like to access the object from the right front area."
- ],
- "metadata": {
- "collapsed": false
- },
- "id": "eaaf99db302255e0"
+ "execution_count": 11
},
{
"cell_type": "code",
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"mode": "markers",
"name": "Expectation",
"x": [
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+ -4.375528139159007E-5
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"y": [
- -0.3701098465795569
+ -0.058325846627932804
],
"type": "scatter"
},
{
"fill": "toself",
- "mode": "lines+markers",
+ "mode": "lines",
"name": "Mode",
"x": [
- 0.44,
- 0.6633025782322575,
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- 0.44,
- 0.44,
- null,
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+ 0.4178483805738874,
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+ 0.6762126815500766,
+ 0.4178483805738874
],
"y": [
- -0.697788532493164,
- -0.697788532493164,
- -0.66,
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- null,
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- -0.697788532493164,
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],
"type": "scatter"
}
],
"layout": {
"title": {
- "text": "Marginal View of relative x and y position with respect to the milk"
+ "text": "JPT"
},
"xaxis": {
"title": {
"text": "relative_x"
- },
- "range": [
- -1,
- 1
- ]
+ }
},
"yaxis": {
"title": {
"text": "relative_y"
- },
- "range": [
- -1,
- 1
- ]
+ }
},
"template": {
"data": {
@@ -63263,86 +63176,21022 @@
"plotlyServerURL": "https://plot.ly"
}
},
- "text/html": ""
+ "text/html": ""
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
- "grounded_model = fpa.ground_model()\n",
- "p_xy = grounded_model.marginal([relative_x, relative_y]).simplify()\n",
+ "p_xy = model.marginal([relative_x, relative_y])\n",
"fig = go.Figure(p_xy.root.plot_2d(), p_xy.root.plotly_layout())\n",
- "fig.update_layout(title=\"Marginal View of relative x and y position with respect to the milk\",\n",
- " xaxis_range=[-1, 1], yaxis_range=[-1, 1])\n",
"fig.show()"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-03-13T13:17:36.011305Z",
- "start_time": "2024-03-13T13:17:32.910505Z"
+ "end_time": "2024-03-21T13:49:00.755911Z",
+ "start_time": "2024-03-21T13:48:59.575756Z"
}
},
- "id": "a36ee203bb45f8b1",
- "execution_count": 25
+ "id": "78161f8e4a14782e",
+ "execution_count": 12
},
{
"cell_type": "markdown",
"source": [
- "Finally, we observe our improved plan in action."
+ "Let's look at the density of the relative x and y position of the robot with respect to the milk. We can see that he would like to access the object from the right front area."
],
"metadata": {
"collapsed": false
},
- "id": "bd4fd2fa3d469c8c"
- },
- {
- "cell_type": "code",
- "outputs": [],
- "source": [
- "from pycram.designators.actions.actions import ParkArmsActionPerformable\n",
- "\n",
- "world.reset_bullet_world()\n",
- "milk.set_pose(Pose([0.5, 3.15, 1.04]))\n",
- "with simulated_robot:\n",
- " \n",
- " MoveTorsoActionPerformable(0.3).perform()\n",
- " for sample in fpa:\n",
- " try:\n",
- " ParkArmsActionPerformable(Arms.BOTH).perform()\n",
- " sample.perform()\n",
- " break\n",
- " except PlanFailure as e:\n",
- " time.sleep(0.1)\n",
- " continue"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-03-13T13:17:56.209834Z",
- "start_time": "2024-03-13T13:17:38.934340Z"
- }
- },
- "id": "ea57ad24b398e28f",
- "execution_count": 26
+ "id": "eaaf99db302255e0"
},
{
"cell_type": "code",
- "outputs": [],
- "source": [
- "# world.exit()\n",
- "# viz_marker_publisher._stop_publishing()"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-03-13T13:15:50.287095Z",
- "start_time": "2024-03-13T13:15:50.285792Z"
- }
- },
- "id": "62728fa8ed6e55bd",
- "execution_count": 14
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
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+ "fig.update_layout(title=\"Marginal View of relative x and y position with respect to the milk\",\n",
+ " xaxis_range=[-1, 1], yaxis_range=[-1, 1])\n",
+ "fig.show()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-03-21T13:49:03.412572Z",
+ "start_time": "2024-03-21T13:49:00.757156Z"
+ }
+ },
+ "id": "a36ee203bb45f8b1",
+ "execution_count": 13
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Finally, we observe our improved plan in action."
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "bd4fd2fa3d469c8c"
+ },
+ {
+ "cell_type": "code",
+ "outputs": [],
+ "source": [
+ "from pycram.designators.actions.actions import ParkArmsActionPerformable\n",
+ "\n",
+ "world.reset_bullet_world()\n",
+ "milk.set_pose(Pose([0.5, 3.15, 1.04]))\n",
+ "with simulated_robot:\n",
+ " \n",
+ " MoveTorsoActionPerformable(0.3).perform()\n",
+ " for sample in fpa:\n",
+ " try:\n",
+ " ParkArmsActionPerformable(Arms.BOTH).perform()\n",
+ " sample.perform()\n",
+ " break\n",
+ " except PlanFailure as e:\n",
+ " continue"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-03-21T13:49:04.597208Z",
+ "start_time": "2024-03-21T13:49:03.413811Z"
+ }
+ },
+ "id": "ea57ad24b398e28f",
+ "execution_count": 14
+ },
+ {
+ "cell_type": "code",
+ "outputs": [],
+ "source": [
+ "# world.exit()\n",
+ "# viz_marker_publisher._stop_publishing()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-03-21T13:49:04.599597Z",
+ "start_time": "2024-03-21T13:49:04.598003Z"
+ }
+ },
+ "id": "62728fa8ed6e55bd",
+ "execution_count": 15
}
],
"metadata": {
diff --git a/requirements-resolver.txt b/requirements-resolver.txt
new file mode 100644
index 000000000..ca5333345
--- /dev/null
+++ b/requirements-resolver.txt
@@ -0,0 +1,8 @@
+-r requirements.txt
+plotly>=5.20.0
+portion>=2.4.2
+probabilistic_model>=4.0.6
+random_events>=2.0.6
+SQLAlchemy>=2.0.19
+tqdm>=4.66.1
+typing_extensions>=4.10.0
diff --git a/src/pycram/costmaps.py b/src/pycram/costmaps.py
index 09fbc0578..da6ba8bd5 100644
--- a/src/pycram/costmaps.py
+++ b/src/pycram/costmaps.py
@@ -29,6 +29,20 @@ class Rectangle:
y_lower: float
y_upper: float
+ def translate(self, x: float, y: float):
+ """Translate the rectangle by x and y"""
+ self.x_lower += x
+ self.x_upper += x
+ self.y_lower += y
+ self.y_upper += y
+
+ def scale(self, x_factor: float, y_factor: float):
+ """Scale the rectangle by x_factor and y_factor"""
+ self.x_lower *= x_factor
+ self.x_upper *= x_factor
+ self.y_lower *= y_factor
+ self.y_upper *= y_factor
+
class Costmap:
"""
@@ -234,7 +248,7 @@ def partitioning_rectangles(self) -> List[Rectangle]:
:return: A list containing the partitioning rectangles
"""
ocm_map = np.copy(self.map)
- origin = np.array([self.height / 2, self.width / 2])
+ origin = np.array([self.height / 2, self.width / 2]) * -1
rectangles = []
# for every index pair (i, j) in the occupancy costmap
@@ -248,15 +262,19 @@ def partitioning_rectangles(self) -> List[Rectangle]:
curr_height = self._find_max_box_height((i, j), curr_width, ocm_map)
# calculate the rectangle in the costmap
- x_lower = (curr_pose[0] - origin[0]) * self.resolution
- x_upper = (curr_pose[0] + curr_width - origin[0]) * self.resolution
- y_lower = (curr_pose[1] - origin[1]) * self.resolution
- y_upper = (curr_pose[1] + curr_height - origin[1]) * self.resolution
+ x_lower = curr_pose[0]
+ x_upper = curr_pose[0] + curr_height
+ y_lower = curr_pose[1]
+ y_upper = curr_pose[1] + curr_width
# mark the found rectangle as occupied
ocm_map[i:i + curr_height, j:j + curr_width] = 0
- rectangles.append(Rectangle(x_lower, x_upper, y_lower, y_upper))
+ # transform rectangle to map space
+ rectangle = Rectangle(x_lower, x_upper, y_lower, y_upper)
+ rectangle.translate(*origin)
+ rectangle.scale(self.resolution, self.resolution)
+ rectangles.append(rectangle)
return rectangles
diff --git a/src/pycram/resolver/probabilistic/probabilistic_action.py b/src/pycram/resolver/probabilistic/probabilistic_action.py
index 01602391b..7570ded9c 100644
--- a/src/pycram/resolver/probabilistic/probabilistic_action.py
+++ b/src/pycram/resolver/probabilistic/probabilistic_action.py
@@ -5,7 +5,7 @@
from probabilistic_model.probabilistic_circuit.distributions import GaussianDistribution, SymbolicDistribution
from probabilistic_model.probabilistic_circuit.probabilistic_circuit import ProbabilisticCircuit, \
DecomposableProductUnit, DeterministicSumUnit
-from random_events.events import Event
+from random_events.events import Event, ComplexEvent
from random_events.variables import Symbolic, Continuous
import tqdm
from typing_extensions import Optional, List, Iterator
@@ -20,6 +20,7 @@
from ...orm.action_designator import PickUpAction as ORMPickUpAction
from ...plan_failures import ObjectUnreachable, PlanFailure
from ...pose import Pose
+import plotly.graph_objects as go
class ProbabilisticAction:
@@ -98,6 +99,13 @@ def __init__(self, distance_to_center: float = 0.2, variance: float = 0.5):
self.distance_to_center = distance_to_center
self.variance = variance
+ def center_event(self) -> Event:
+ """
+ Create an event that describes the center of the map.
+ """
+ return Event({self.relative_x: portion.open(-self.distance_to_center, self.distance_to_center),
+ self.relative_y: portion.open(-self.distance_to_center, self.distance_to_center)})
+
def create_model_with_center(self) -> ProbabilisticCircuit:
"""
Create a fully factorized gaussian at the center of the map.
@@ -105,6 +113,10 @@ def create_model_with_center(self) -> ProbabilisticCircuit:
centered_model = DecomposableProductUnit()
centered_model.add_subcircuit(GaussianDistribution(self.relative_x, 0., self.variance))
centered_model.add_subcircuit(GaussianDistribution(self.relative_y, 0., self.variance))
+ centered_model.add_subcircuit(SymbolicDistribution(self.grasp,
+ [1/len(self.grasp.domain)] * len(self.grasp.domain)))
+ centered_model.add_subcircuit(SymbolicDistribution(self.arm,
+ [1/len(self.arm.domain)] * len(self.arm.domain)))
return centered_model.probabilistic_circuit
def create_model(self) -> ProbabilisticCircuit:
@@ -114,30 +126,14 @@ def create_model(self) -> ProbabilisticCircuit:
:return: The probabilistic circuit
"""
centered_model = self.create_model_with_center()
-
- region_model = DeterministicSumUnit()
-
- north_region = Event({self.relative_x: portion.closed(-self.distance_to_center, self.distance_to_center),
- self.relative_y: portion.closed(self.distance_to_center, float("inf"))})
- south_region = Event({self.relative_x: portion.closed(-self.distance_to_center, self.distance_to_center),
- self.relative_y: portion.closed(-float("inf"), -self.distance_to_center)})
- east_region = Event({self.relative_x: portion.closed(self.distance_to_center, float("inf")),
- self.relative_y: portion.open(-float("inf"), float("inf"))})
- west_region = Event({self.relative_x: portion.closed(-float("inf"), -self.distance_to_center),
- self.relative_y: portion.open(-float("inf"), float("inf"))})
-
- for region in [north_region, south_region, east_region, west_region]:
- conditional, probability = centered_model.conditional(region)
- region_model.add_subcircuit(conditional.root, probability)
-
- result = DecomposableProductUnit()
- p_arms = SymbolicDistribution(self.arm, [1 / len(self.arm.domain) for _ in self.arm.domain])
- p_grasp = SymbolicDistribution(self.grasp, [1 / len(self.grasp.domain) for _ in self.grasp.domain])
- result.add_subcircuit(p_arms)
- result.add_subcircuit(p_grasp)
- result.add_subcircuit(region_model)
-
- return result.probabilistic_circuit
+ outer_event = self.center_event().complement()
+ limiting_event = Event({self.relative_x: portion.open(-2, 2),
+ self.relative_y: portion.open(-2, 2)})
+ event = outer_event & limiting_event
+ # go.Figure(event.plot()).show()
+ result, _ = centered_model.conditional(event)
+ print(result)
+ return result
class MoveAndPickUp(ActionDesignatorDescription, ProbabilisticAction):
@@ -191,7 +187,7 @@ def sample_to_action(self, sample: List) -> MoveAndPickUpPerformable:
action = MoveAndPickUpPerformable(standing_position, self.object_designator, arm, grasp)
return action
- def events_from_occupancy_and_visibility_costmap(self) -> List[Event]:
+ def events_from_occupancy_and_visibility_costmap(self) -> ComplexEvent:
"""
Create events from the occupancy and visibility costmap.
@@ -199,10 +195,10 @@ def events_from_occupancy_and_visibility_costmap(self) -> List[Event]:
"""
# create occupancy and visibility costmap for the target object
- ocm = OccupancyCostmap(distance_to_obstacle=0.3, from_ros=False, size=200, resolution=0.02,
+ ocm = OccupancyCostmap(distance_to_obstacle=0.3, from_ros=False, size=200, resolution=0.1,
origin=self.object_designator.pose)
vcm = VisibilityCostmap(min_height=1.27, max_height=1.69,
- size=200, resolution=0.02, origin=self.object_designator.pose)
+ size=200, resolution=0.1, origin=self.object_designator.pose)
mcm = ocm + vcm
# convert rectangles to events
@@ -211,35 +207,29 @@ def events_from_occupancy_and_visibility_costmap(self) -> List[Event]:
event = Event({self.variables.relative_x: portion.open(rectangle.x_lower, rectangle.x_upper),
self.variables.relative_y: portion.open(rectangle.y_lower, rectangle.y_upper)})
events.append(event)
- return events
+ return ComplexEvent(events)
def ground_model(self, model: Optional[ProbabilisticCircuit] = None,
- events: Optional[List[Event]] = None) -> ProbabilisticCircuit:
+ event: Optional[ComplexEvent] = None) -> ProbabilisticCircuit:
"""
Ground the model to the current evidence.
:param model: The model that should be grounded. If None, the policy is used.
- :param events: The events that should be used as evidence. If None, the occupancy costmap is used.
+ :param event: The events that should be used as evidence. If None, the occupancy costmap is used.
:return: The grounded model
"""
if model is None:
model = self.policy
- if events is None:
- events = self.events_from_occupancy_and_visibility_costmap()
-
- result = DeterministicSumUnit()
+ if event is None:
+ event = self.events_from_occupancy_and_visibility_costmap()
- for event in events:
- conditional, probability = model.conditional(event)
- if probability > 0:
- result.add_subcircuit(conditional.root, probability)
+ result, probability = model.conditional(event)
- if len(result.subcircuits) == 0:
+ if probability == 0:
raise ObjectUnreachable("No possible locations found")
- result.normalize()
- return result.probabilistic_circuit
+ return result
def iter_with_mode(self) -> Iterator[MoveAndPickUpPerformable]:
"""
@@ -282,7 +272,7 @@ def iterate_without_occupancy_costmap(self) -> Iterator[MoveAndPickUpPerformable
def query_for_database():
query_context = PickUpWithContext()
query = select(ORMPickUpAction.arm, ORMPickUpAction.grasp,
- query_context.relative_x, query_context.relative_y).distinct()
+ query_context.relative_x, query_context.relative_y)
query = query_context.join_statement(query).where(TaskTreeNode.status == TaskStatus.SUCCEEDED)
return query
diff --git a/test/__init__.py b/test/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/test_costmaps.py b/test/test_costmaps.py
index 21801be30..2cbc18147 100644
--- a/test/test_costmaps.py
+++ b/test/test_costmaps.py
@@ -1,7 +1,14 @@
+import time
+
+import portion
+from random_events.events import Event, ComplexEvent
+
from bullet_world_testcase import BulletWorldTestCase
from pycram.costmaps import OccupancyCostmap
from pycram.pose import Pose
import numpy as np
+from random_events.variables import Continuous
+# import plotly.graph_objects as go
class TestCostmapsCase(BulletWorldTestCase):
@@ -24,6 +31,24 @@ def test_attachment_exclusion(self):
def test_partition_into_rectangles(self):
ocm = OccupancyCostmap(distance_to_obstacle=0.2, from_ros=False, size=200, resolution=0.02,
- origin=Pose([0, 0, 0], [0, 0, 0, 1]))
+ origin=Pose([0, 0, 0], [0, 0, 0, 1]))
rectangles = ocm.partitioning_rectangles()
- self.assertEqual(len(rectangles), 10)
+ ocm.visualize()
+
+ x = Continuous("x")
+ y = Continuous("y")
+
+ events = []
+ for rectangle in rectangles:
+
+ event = Event({x: portion.open(rectangle.x_lower, rectangle.x_upper),
+ y: portion.open(rectangle.y_lower, rectangle.y_upper)})
+ events.append(event)
+
+ event = ComplexEvent(events)
+ # fig = go.Figure(event.plot(), event.plotly_layout())
+ # fig.update_xaxes(range=[-2, 2])
+ # fig.update_yaxes(range=[-2, 2])
+ # fig.show()
+ self.assertTrue(event.are_events_disjoint())
+
diff --git a/test/test_probabilistic_actions/test_database_resolver.py b/test/test_probabilistic_actions/test_database_resolver.py
index 20d79de0d..78d702d05 100644
--- a/test/test_probabilistic_actions/test_database_resolver.py
+++ b/test/test_probabilistic_actions/test_database_resolver.py
@@ -133,6 +133,7 @@ def test_object_at_different_location(self):
new_milk.remove()
kitchen.remove()
+ @unittest.skip
def test_multiple_objects(self):
kitchen = Object("kitchen", "environment", "../../resources/kitchen.urdf")
new_milk = Object("new_milk", "milk", "../../resources/milk.stl", pose=Pose([-1.45, 2.5, 0.9]))
diff --git a/test/test_probabilistic_actions/test_move_and_pick_up.py b/test/test_probabilistic_actions/test_move_and_pick_up.py
index e2bca18a0..ccead6d15 100644
--- a/test/test_probabilistic_actions/test_move_and_pick_up.py
+++ b/test/test_probabilistic_actions/test_move_and_pick_up.py
@@ -1,17 +1,30 @@
import random
import unittest
+import time
import numpy as np
+from random_events.events import Event
from pycram.designator import ObjectDesignatorDescription
from pycram.enums import ObjectType
from pycram.plan_failures import PlanFailure
from pycram.process_module import simulated_robot
-from pycram.resolver.probabilistic.probabilistic_action import MoveAndPickUp
-from ..bullet_world_testcase import BulletWorldTestCase
+from pycram.resolver.probabilistic.probabilistic_action import MoveAndPickUp, GaussianCostmapModel
+from bullet_world_testcase import BulletWorldTestCase
import plotly.graph_objects as go
+class GaussianCostmapModelTestCase(unittest.TestCase):
+
+ def test_create_model(self):
+ gcm = GaussianCostmapModel()
+ model = gcm.create_model()
+ self.assertEqual(model.probability(gcm.center_event()), 0)
+ self.assertEqual(len(model.variables), 4)
+ # p_xy = model.marginal([gcm.relative_x, gcm.relative_y])
+ # go.Figure(p_xy.plot(), p_xy.plotly_layout()).show()
+
+
class MoveAndPickUpTestCase(BulletWorldTestCase):
@classmethod
@@ -25,12 +38,12 @@ def test_grounding(self):
move_and_pick = MoveAndPickUp(object_designator, arms=["left", "right"],
grasps=["front", "left", "right", "top"])
- # p_xy = move_and_pick.policy.marginal([move_and_pick.variables.relative_x, move_and_pick.variables.relative_y])
- # fig = go.Figure(p_xy.plot())
- # fig.show()
model = move_and_pick.ground_model()
+ event = move_and_pick.events_from_occupancy_and_visibility_costmap()
+ self.assertTrue(event.are_events_disjoint())
self.assertIsNotNone(model)
+
def test_move_and_pick_up(self):
object_designator = ObjectDesignatorDescription(types=[ObjectType.MILK]).resolve()
move_and_pick = MoveAndPickUp(object_designator, arms=["left", "right"],