Yuval Tassa
aceb52bd09
- `mjSTATE_FULLPHYSICS` as state spec, enabling divergence detection by inspecting time. - User-defined control spec. - Stop squeezing: outputs always have dim=3. PiperOrigin-RevId: 600445256 Change-Id: I4466e88929cb7081e1c94968a5cfe10485bb7475
591 lines
20 KiB
Python
591 lines
20 KiB
Python
# Copyright 2022 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""tests for rollout function."""
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import concurrent.futures
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import threading
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from absl.testing import absltest
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from absl.testing import parameterized
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import mujoco
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from mujoco import rollout
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import numpy as np
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# -------------------------- models used for testing ---------------------------
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TEST_XML = r"""
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<mujoco>
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<worldbody>
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<light pos="0 0 2"/>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 .1">
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<joint name="yaw" axis="0 0 1"/>
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<joint name="pitch" axis="0 1 0"/>
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<geom type="capsule" size=".02" fromto="0 0 0 1 0 0"/>
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<geom type="box" pos="1 0 0" size=".1 .1 .1"/>
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<site name="site" pos="1 0 0"/>
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</body>
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</worldbody>
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<actuator>
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<general joint="pitch" gainprm="100"/>
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<general joint="yaw" dyntype="filter" dynprm="1" gainprm="100"/>
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</actuator>
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<sensor>
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<accelerometer site="site"/>
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</sensor>
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</mujoco>
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"""
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TEST_XML_NO_SENSORS = r"""
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<mujoco>
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<worldbody>
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<light pos="0 0 2"/>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 .1">
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<joint name="yaw" axis="0 0 1"/>
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<joint name="pitch" axis="0 1 0"/>
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<geom type="capsule" size=".02" fromto="0 0 0 1 0 0"/>
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<geom type="box" pos="1 0 0" size=".1 .1 .1"/>
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<site name="site" pos="1 0 0"/>
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</body>
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</worldbody>
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<actuator>
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<general joint="pitch" gainprm="100"/>
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<general joint="yaw" dyntype="filter" dynprm="1" gainprm="100"/>
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</actuator>
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</mujoco>
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"""
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TEST_XML_NO_ACTUATORS = r"""
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<mujoco>
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<worldbody>
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<light pos="0 0 2"/>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 .1">
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<joint name="yaw" axis="0 0 1"/>
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<joint name="pitch" axis="0 1 0"/>
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<geom type="capsule" size=".02" fromto="0 0 0 1 0 0"/>
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<geom type="box" pos="1 0 0" size=".1 .1 .1"/>
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<site name="site" pos="1 0 0"/>
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</body>
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</worldbody>
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<sensor>
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<accelerometer site="site"/>
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</sensor>
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</mujoco>
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"""
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TEST_XML_MOCAP = r"""
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<mujoco>
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<worldbody>
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<body name="1" mocap="true">
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</body>
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<body name="2" mocap="true">
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</body>
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</worldbody>
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<sensor>
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<framepos objtype="xbody" objname="1"/>
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<framequat objtype="xbody" objname="1"/>
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</sensor>
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</mujoco>
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"""
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TEST_XML_EMPTY = r"""
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<mujoco>
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</mujoco>
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"""
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TEST_XML_DIVERGE = r"""
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<mujoco>
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<option>
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<flag gravity="disable"/>
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</option>
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<worldbody>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 -.3" euler="30 45 90">
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<freejoint/>
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<geom type="box" size=".1 .2 .4"/>
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</body>
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</worldbody>
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<keyframe>
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<key name="non-diverging" qpos="0 0 .5 1 0 0 0"/>
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</keyframe>
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</mujoco>
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"""
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ALL_MODELS = {'TEST_XML': TEST_XML,
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'TEST_XML_NO_SENSORS': TEST_XML_NO_SENSORS,
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'TEST_XML_NO_ACTUATORS': TEST_XML_NO_ACTUATORS,
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'TEST_XML_EMPTY': TEST_XML_EMPTY}
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# ------------------------------ tests -----------------------------------------
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class MuJoCoRolloutTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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np.random.seed(42)
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# ----------------------------- test basic operation
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@parameterized.parameters(ALL_MODELS.keys())
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def test_single_step(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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initial_state = np.random.randn(nstate)
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control = np.random.randn(model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, control)
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mujoco.mj_resetData(model, data)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_one_rollout(self, model_name):
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nstep = 3 # number of timesteps
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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initial_state = np.random.randn(nstate)
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control = np.random.randn(nstep, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, control)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_multi_step(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 5 # number of rollouts
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nstep = 1 # number of steps
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initial_state = np.random.randn(nroll, nstate)
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control = np.random.randn(nroll, nstep, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, control)
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mujoco.mj_resetData(model, data)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_one_rollout_fixed_ctrl(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 1 # number of rollouts
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nstep = 3 # number of steps
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initial_state = np.random.randn(nstate)
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control = np.random.randn(model.nu)
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state = np.empty((nroll, nstep, nstate))
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sensordata = np.empty((nroll, nstep, model.nsensordata))
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rollout.rollout(model, data, initial_state, control,
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state=state, sensordata=sensordata)
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control = np.tile(control, (nstep, 1))
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_multi_rollout(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 2 # number of initial states
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nstep = 3 # number of timesteps
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initial_state = np.random.randn(nroll, nstate)
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control = np.random.randn(nroll, nstep, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, control)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_multi_rollout_fixed_ctrl_infer_from_output(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 2 # number of rollouts
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nstep = 3 # number of timesteps
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initial_state = np.random.randn(nroll, nstate)
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control = np.random.randn(nroll, 1, model.nu)
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state = np.empty((nroll, nstep, nstate))
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state, sensordata = rollout.rollout(model, data, initial_state, control,
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state=state)
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control = np.repeat(control, nstep, axis=1)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_py_rollout_generalized_control(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 4 # number of rollouts
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nstep = 3 # number of timesteps
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initial_state = np.random.randn(nroll, nstate)
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control_spec = (mujoco.mjtState.mjSTATE_CTRL |
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mujoco.mjtState.mjSTATE_QFRC_APPLIED |
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mujoco.mjtState.mjSTATE_XFRC_APPLIED)
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ncontrol = mujoco.mj_stateSize(model, control_spec)
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control = np.random.randn(nroll, nstep, ncontrol)
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state, sensordata = rollout.rollout(model, data, initial_state, control,
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control_spec=control_spec)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control,
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control_spec=control_spec)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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def test_detect_divergence(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML_DIVERGE)
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 4 # number of rollouts
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initial_state = np.empty((nroll, nstate))
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# get diverging (0, 2) and non-diverging (1, 3) states
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mujoco.mj_getState(model, data, initial_state[0],
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mujoco.mjtState.mjSTATE_FULLPHYSICS)
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mujoco.mj_getState(model, data, initial_state[2],
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mujoco.mjtState.mjSTATE_FULLPHYSICS)
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mujoco.mj_resetDataKeyframe(model, data, 0) # keyframe 0 does not diverge
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mujoco.mj_getState(model, data, initial_state[1],
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mujoco.mjtState.mjSTATE_FULLPHYSICS)
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mujoco.mj_getState(model, data, initial_state[3],
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mujoco.mjtState.mjSTATE_FULLPHYSICS)
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nstep = 10000 # divergence after ~15s, timestep = 2e-3
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state = np.random.randn(nroll, nstep, nstate)
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rollout.rollout(model, data, initial_state, state=state)
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# initial_state[0,2] diverged, final timesteps are identical
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assert state[0][-1][0] == state[0][-2][0]
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assert state[2][-1][0] == state[2][-2][0]
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# initial_state[1,3] did not diverge, final timesteps are different
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assert state[1][-1][0] != state[1][-2][0]
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assert state[3][-1][0] != state[3][-2][0]
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# ----------------------------- test threaded operation
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def test_threading(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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num_workers = 32
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nroll = 10000
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nstep = 5
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initial_state = np.random.randn(nroll, nstate)
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state = np.empty((nroll, nstep, nstate))
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sensordata = np.empty((nroll, nstep, model.nsensordata))
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control = np.random.randn(nroll, nstep, model.nu)
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thread_local = threading.local()
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def thread_initializer():
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thread_local.data = mujoco.MjData(model)
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def call_rollout(initial_state, control, state, sensordata):
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rollout.rollout(model, thread_local.data, initial_state, control,
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skip_checks=True, nroll=initial_state.shape[0],
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nstep=nstep, state=state, sensordata=sensordata)
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n = nroll // num_workers # integer division
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chunks = [] # a list of tuples, one per worker
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for i in range(num_workers-1):
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chunks.append((initial_state[i*n:(i+1)*n],
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control[i*n:(i+1)*n],
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state[i*n:(i+1)*n],
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sensordata[i*n:(i+1)*n]))
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# last chunk, absorbing the remainder:
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chunks.append((initial_state[(num_workers-1)*n:],
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control[(num_workers-1)*n:],
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state[(num_workers-1)*n:],
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sensordata[(num_workers-1)*n:]))
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with concurrent.futures.ThreadPoolExecutor(
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max_workers=num_workers, initializer=thread_initializer) as executor:
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futures = []
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for chunk in chunks:
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futures.append(executor.submit(call_rollout, *chunk))
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for future in concurrent.futures.as_completed(futures):
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future.result()
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data = mujoco.MjData(model)
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py_state, py_sensordata = py_rollout(model, data, initial_state, control)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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# ---------------------------- test advanced operation
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def test_warmstart(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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# take one step, save the state
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state0 = np.zeros(nstate)
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control = np.zeros(model.nu)
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state1, _ = step(model, data, state0, control)
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# save qacc_warmstart
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initial_warmstart = data.qacc_warmstart.copy()
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# take one more step (uses correct warmstart)
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state2, _ = step(model, data, state1[0], control)
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# take step using rollout, don't take warmstart into account
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state, _ = rollout.rollout(model, data, state1[0], control)
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# assert that stepping without warmstarts is not exact
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np.testing.assert_raises(AssertionError,
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np.testing.assert_array_equal, state, state2)
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# take step using rollout, take warmstart into account
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state, _ = rollout.rollout(model, data, state1, control,
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initial_warmstart=initial_warmstart)
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# assert exact equality
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np.testing.assert_array_equal(state, np.expand_dims(state2, axis=0))
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def test_mocap(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML_MOCAP)
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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initial_state = np.zeros(nstate)
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control_spec = (mujoco.mjtState.mjSTATE_MOCAP_POS |
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mujoco.mjtState.mjSTATE_MOCAP_QUAT)
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pos1 = np.array((1., 2., 3.))
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quat1 = np.array((1., 2., 3., 4.))
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quat1 /= np.linalg.norm(quat1)
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pos2 = np.array((2., 3., 4.))
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quat2 = np.array((2., 3., 4., 5.))
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quat2 /= np.linalg.norm(quat2)
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control = np.hstack((pos1, pos2, quat1, quat2))
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_, sensordata = rollout.rollout(model, data, initial_state, control,
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control_spec=control_spec)
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np.testing.assert_array_almost_equal(sensordata[0][0][:3], pos1)
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np.testing.assert_array_almost_equal(sensordata[0][0][3:], quat1)
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# ---------------------------- test correctness
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def test_intercept_mj_errors(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
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data = mujoco.MjData(model)
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nroll = 1
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nstep = 3
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initial_state = np.zeros((nroll, nstate))
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ctrl = np.zeros((nroll, nstep, model.nu))
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model.opt.solver = 10 # invalid solver type
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with self.assertRaisesWithLiteralMatch(
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mujoco.FatalError, 'mj_fwdConstraint: unknown solver type 10'):
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rollout.rollout(model, data, initial_state, ctrl)
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def test_invalid(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
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data = mujoco.MjData(model)
|
|
|
|
nroll = 1
|
|
|
|
initial_state = np.zeros((nroll, nstate))
|
|
|
|
control = 'string'
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'control must be a numpy array or float'):
|
|
rollout.rollout(model, data, initial_state, control)
|
|
|
|
control = np.zeros((2, 3, 4, 5))
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'control can have at most 3 dimensions'):
|
|
rollout.rollout(model, data, initial_state, control)
|
|
|
|
def test_bad_sizes(self):
|
|
model = mujoco.MjModel.from_xml_string(TEST_XML)
|
|
nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
data = mujoco.MjData(model)
|
|
|
|
nroll = 1
|
|
nstep = 3
|
|
|
|
initial_state = np.random.randn(nroll, nstate + 1)
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'trailing dimension of initial_state must be 6, got 7'):
|
|
rollout.rollout(model, data, initial_state)
|
|
|
|
initial_state = np.random.randn(nroll, nstate)
|
|
control = np.random.randn(1, nstep, model.nu + 1)
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'trailing dimension of control must be 2, got 3'):
|
|
rollout.rollout(model, data, initial_state, control)
|
|
|
|
control = np.random.randn(nroll, nstep, model.nu)
|
|
state = np.random.randn(nroll, nstep+1, nstate) # incompatible nstep
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'dimension 1 inferred as 3 but state has 4'):
|
|
rollout.rollout(model, data, initial_state, control, state=state)
|
|
|
|
initial_state = np.random.randn(nroll, nstate)
|
|
control = np.random.randn(nroll, nstep, model.nu)
|
|
bad_spec = mujoco.mjtState.mjSTATE_ACT
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'control_spec can only contain bits in mjSTATE_USER'):
|
|
rollout.rollout(model, data, initial_state, control,
|
|
control_spec=bad_spec)
|
|
|
|
def test_stateless(self):
|
|
model = mujoco.MjModel.from_xml_string(TEST_XML)
|
|
nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
data = mujoco.MjData(model)
|
|
|
|
# step with a clean mjData
|
|
initial_state = np.random.randn(nstate)
|
|
control = np.random.randn(3, 3, model.nu)
|
|
state, sensordata = rollout.rollout(model, data, initial_state, control)
|
|
|
|
# fill user fields with random values
|
|
for attr in [
|
|
'ctrl',
|
|
'qfrc_applied',
|
|
'xfrc_applied',
|
|
'mocap_pos',
|
|
'mocap_quat',
|
|
]:
|
|
setattr(data, attr, np.random.randn(*getattr(data, attr).shape))
|
|
|
|
# roll out again
|
|
state2, sensordata2 = rollout.rollout(model, data, initial_state, control)
|
|
|
|
# assert that we still get the same outputs
|
|
np.testing.assert_array_equal(state, state2)
|
|
np.testing.assert_array_equal(sensordata, sensordata2)
|
|
|
|
|
|
# -------------- Python implementation of rollout functionality ----------------
|
|
|
|
|
|
def get_state(model, data):
|
|
nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
state = np.empty(nstate)
|
|
mujoco.mj_getState(model, data, state, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
return state.reshape((1, nstate))
|
|
|
|
|
|
def step(model, data, state, control,
|
|
control_spec=mujoco.mjtState.mjSTATE_CTRL):
|
|
if state is not None:
|
|
mujoco.mj_setState(model, data, state, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
mujoco.mj_setState(model, data, control, control_spec)
|
|
mujoco.mj_step(model, data)
|
|
return (get_state(model, data), data.sensordata)
|
|
|
|
|
|
def one_rollout(model, data, initial_state, control,
|
|
control_spec=mujoco.mjtState.mjSTATE_CTRL):
|
|
nstep = control.shape[0]
|
|
nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
state = np.empty((nstep, nstate))
|
|
sensordata = np.empty((nstep, model.nsensordata))
|
|
|
|
mujoco.mj_resetData(model, data)
|
|
for t in range(nstep):
|
|
state[t], sensordata[t] = step(model, data,
|
|
initial_state if t == 0 else None,
|
|
control[t], control_spec)
|
|
return state, sensordata
|
|
|
|
|
|
def ensure_2d(arg):
|
|
if arg is None:
|
|
return None
|
|
else:
|
|
return np.ascontiguousarray(np.atleast_2d(arg), dtype=np.float64)
|
|
|
|
|
|
def ensure_3d(arg):
|
|
if arg is None:
|
|
return None
|
|
else:
|
|
# np.atleast_3d adds both leading and trailing dims, we want only leading
|
|
if arg.ndim == 0:
|
|
arg = arg[np.newaxis, np.newaxis, np.newaxis, ...]
|
|
elif arg.ndim == 1:
|
|
arg = arg[np.newaxis, np.newaxis, ...]
|
|
elif arg.ndim == 2:
|
|
arg = arg[np.newaxis, ...]
|
|
return np.ascontiguousarray(arg, dtype=np.float64)
|
|
|
|
|
|
def py_rollout(model, data, initial_state, control,
|
|
control_spec=mujoco.mjtState.mjSTATE_CTRL):
|
|
initial_state = ensure_2d(initial_state)
|
|
control = ensure_3d(control)
|
|
nroll = initial_state.shape[0]
|
|
nstep = control.shape[1]
|
|
nstate = mujoco.mj_stateSize(model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
|
|
|
|
state = np.empty((nroll, nstep, nstate))
|
|
sensordata = np.empty((nroll, nstep, model.nsensordata))
|
|
for r in range(nroll):
|
|
state_r, sensordata_r = one_rollout(
|
|
model, data, initial_state[r], control[r], control_spec
|
|
)
|
|
state[r] = state_r
|
|
sensordata[r] = sensordata_r
|
|
return state, sensordata
|
|
|
|
|
|
if __name__ == '__main__':
|
|
absltest.main()
|