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 @@ "data": [ { "hovertext": [ - "Likelihood: 0.2960051973772738", - 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" }, "metadata": {}, "output_type": "display_data" @@ -21157,8 +21126,8 @@ "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" } }, "id": "24bc79646157a9bf", @@ -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": { "collapsed": false, "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" - } - ], - "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 - }, { "cell_type": "markdown", "source": [ @@ -21250,27 +21193,27 @@ "outputs": [ { "data": { - "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": "
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armgrasprelative_xrelative_y
0rightright-0.0343960.253821
1leftright0.0273720.346807
2rightright0.0513920.319307
3leftright0.0745180.265504
4leftleft0.1749540.857668
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" }, - "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": [ { "hovertext": [ - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - "Likelihood: 0.6087440920448061", - 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" }, "metadata": {}, "output_type": "display_data" @@ -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", @@ -42352,20016 +42287,20016 @@ "data": [ { "hovertext": [ - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 30.091528326798986", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 30.091528326798986", - "Likelihood: 30.091528326798986", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 48.02373812816365", - "Likelihood: 30.091528326798986", - "Likelihood: 48.02373812816365", - "Likelihood: 30.091528326798986", - "Likelihood: 30.091528326798986", - "Likelihood: 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[ - 0.5159507647804554 + -4.375528139159007E-5 ], "y": [ - -0.3701098465795569 + -0.058325846627932804 ], "type": "scatter" }, { "fill": "toself", - "mode": "lines+markers", + "mode": "lines", "name": "Mode", "x": [ - 0.44, - 0.6633025782322575, - 0.6633025782322575, - 0.44, - 0.44, - null, - 0.44, - 0.6633025782322575, - 0.6633025782322575, - 0.44, - 0.44, - null + 0.4178483805738874, + 0.4178483805738874, + 0.6762126815500766, + 0.6762126815500766, + 0.4178483805738874 ], "y": [ - -0.697788532493164, - -0.697788532493164, - -0.66, - -0.66, - -0.697788532493164, - null, - -0.697788532493164, - -0.697788532493164, - -0.66, - -0.66, - -0.697788532493164, - null + 0.15624896603640903, + 0.393362968645088, + 0.393362968645088, + 0.15624896603640903, + 0.15624896603640903 ], "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": { + "data": [ + { + "hovertext": [ + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 0.7111846762867592", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 0.7111846762867592", + "Likelihood: 1.5599843084222236", + "Likelihood: 1.5599843084222236", + "Likelihood: 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"bordercolor": "rgb(17,17,17)", + "tickwidth": 0 + }, + "mapbox": { + "style": "dark" + } + } + } + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "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", + "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": "2024-03-21T13:49:03.412572Z", + "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"],