def rodent_run_gaps(random_state=None):
"""Requires a rodent to run down a corridor with gaps."""
# Build a position-controlled rodent walker.
walker = rodent.Rat(
observable_options={'egocentric_camera': dict(enabled=True)})
# Build a corridor-shaped arena with gaps, where the sizes of the gaps and
# platforms are uniformly randomized.
arena = corr_arenas.GapsCorridor(platform_length=distributions.Uniform(
.4, .8),
gap_length=distributions.Uniform(.05, .2),
corridor_width=2,
corridor_length=40,
aesthetic='outdoor_natural')
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(5, 0, 0),
walker_spawn_rotation=0,
target_velocity=1.0,
contact_termination=False,
terminate_at_height=-0.3,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def test_termination_and_discount(self):
walker = cmu_humanoid.CMUHumanoid()
arena = corridor_arenas.EmptyCorridor()
task = corridor_tasks.RunThroughCorridor(walker, arena)
random_state = np.random.RandomState(12345)
env = composer.Environment(task, random_state=random_state)
env.reset()
zero_action = np.zeros_like(env.physics.data.ctrl)
# Walker starts in upright position.
# Should not trigger failure termination in the first few steps.
for _ in range(5):
env.step(zero_action)
self.assertFalse(task.should_terminate_episode(env.physics))
self.assertEqual(task.get_discount(env.physics), 1)
# Rotate the walker upside down and run the physics until it makes contact.
current_time = env.physics.data.time
walker.shift_pose(env.physics, position=(0, 0, 10), quaternion=(0, 1, 0, 0))
env.physics.forward()
while env.physics.data.ncon == 0:
env.physics.step()
env.physics.data.time = current_time
# Should now trigger a failure termination.
env.step(zero_action)
self.assertTrue(task.should_terminate_episode(env.physics))
self.assertEqual(task.get_discount(env.physics), 0)
def test_walker_is_correctly_reinitialized(
self, position_offset, rotate_180_degrees, use_variations):
walker_spawn_position = position_offset
if not rotate_180_degrees:
walker_spawn_rotation = None
else:
walker_spawn_rotation = np.pi
if use_variations:
walker_spawn_position = deterministic.Constant(position_offset)
walker_spawn_rotation = deterministic.Constant(walker_spawn_rotation)
walker = cmu_humanoid.CMUHumanoid()
arena = corridor_arenas.EmptyCorridor()
task = corridor_tasks.RunThroughCorridor(
walker=walker,
arena=arena,
walker_spawn_position=walker_spawn_position,
walker_spawn_rotation=walker_spawn_rotation)
# Randomize the initial pose and joint positions in order to check that they
# are set correctly by `initialize_episode`.
random_state = np.random.RandomState(12345)
task.initialize_episode_mjcf(random_state)
physics = mjcf.Physics.from_mjcf_model(task.root_entity.mjcf_model)
walker_joints = walker.mjcf_model.find_all('joint')
physics.bind(walker_joints).qpos = random_state.uniform(
size=len(walker_joints))
walker.set_pose(physics,
position=random_state.uniform(size=3),
quaternion=rotations.UniformQuaternion()(random_state))
task.initialize_episode(physics, random_state)
physics.forward()
with self.subTest('Correct joint positions'):
walker_qpos = physics.bind(walker_joints).qpos
if walker.upright_pose.qpos is not None:
np.testing.assert_array_equal(walker_qpos, walker.upright_pose.qpos)
else:
walker_qpos0 = physics.bind(walker_joints).qpos0
np.testing.assert_array_equal(walker_qpos, walker_qpos0)
walker_xpos, walker_xquat = walker.get_pose(physics)
with self.subTest('Correct position'):
expected_xpos = walker.upright_pose.xpos + np.array(position_offset)
np.testing.assert_array_equal(walker_xpos, expected_xpos)
with self.subTest('Correct orientation'):
upright_xquat = walker.upright_pose.xquat.copy()
upright_xquat /= np.linalg.norm(walker.upright_pose.xquat)
if rotate_180_degrees:
expected_xquat = (-upright_xquat[3], -upright_xquat[2],
upright_xquat[1], upright_xquat[0])
else:
expected_xquat = upright_xquat
np.testing.assert_allclose(walker_xquat, expected_xquat)
def cmu_humanoid_run_gaps(random_state=None):
"""Requires a CMU humanoid to run down a corridor with gaps."""
# Build a position-controlled CMU humanoid walker.
walker = cmu_humanoid.CMUHumanoidPositionControlled(
observable_options={'egocentric_camera': dict(enabled=True)})
# Build a corridor-shaped arena with gaps, where the sizes of the gaps and
# platforms are uniformly randomized.
arena = corr_arenas.GapsCorridor(
platform_length=distributions.Uniform(.3, 2.5),
gap_length=distributions.Uniform(.5, 1.25),
corridor_width=10,
corridor_length=100)
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(0.5, 0, 0),
target_velocity=3.0,
physics_timestep=0.005,
control_timestep=0.03)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def walker_run_gaps(random_state=None):
walker = planar_walker.PlanarWalker()
# Build a corridor-shaped arena with gaps, where the sizes of the gaps and
# platforms are uniformly randomized.
arena = corr_arenas.GapsCorridor(
platform_length=distributions.Uniform(1.25, 2.5), # (0.3, 2.5)
gap_length=distributions.Uniform(0.3, 0.7), # (0.5, 1.25)
corridor_width=10,
corridor_length=250)
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(1.0, 0, 0),
stand_height=1.2,
contact_termination=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
# (Chongyi Zheng): redefine reward function
task.get_reward = _walker_get_reward.__get__(task, task.get_reward)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def cmu_humanoid_run_walls(random_state=None):
"""Requires a CMU humanoid to run down a corridor obstructed by walls."""
# Build a position-controlled CMU humanoid walker.
walker = cmu_humanoid.CMUHumanoidPositionControlled(
observable_options={'egocentric_camera': dict(enabled=True)})
# Build a corridor-shaped arena that is obstructed by walls.
arena = corr_arenas.WallsCorridor(wall_gap=4.,
wall_width=distributions.Uniform(1, 7),
wall_height=3.0,
corridor_width=10,
corridor_length=100,
include_initial_padding=False)
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(0.5, 0, 0),
target_velocity=3.0,
physics_timestep=0.005,
control_timestep=0.03)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def ant_run_walls():
walker = ant.Ant()
arena = corr_arenas.WallsCorridor(wall_gap=4.,
wall_width=distributions.Uniform(1, 7),
wall_height=3.0,
corridor_width=10,
corridor_length=250,
include_initial_padding=False)
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(0.5, 0, 0),
walker_spawn_rotation=0,
stand_height=0.2,
contact_termination=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
# (Chongyi Zheng): redefine reward function
# task.get_reward = _ant_get_reward.__get__(task, task.get_reward)
return composer.Environment(time_limit=30,
task=task,
strip_singleton_obs_buffer_dim=True)
def jumping_ball_run_gaps(random_state=None):
walker = jumping_ball.JumpingBallWithHead()
# Build a corridor-shaped arena with gaps, where the sizes of the gaps and
# platforms are uniformly randomized.
arena = corr_arenas.GapsCorridor(
platform_length=distributions.Uniform(1.0, 2.5), # (0.3, 2.5)
gap_length=distributions.Uniform(0.3, 0.7), # (0.5, 1.25)
corridor_width=10,
corridor_length=250)
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(1.0, 0, 0),
target_velocity=3.0,
contact_termination=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def ant_run(random_state=None):
walker = ant.Ant()
arena = corr_arenas.EmptyCorridor()
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(5, 0, 0),
walker_spawn_rotation=0,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def _get_jumping_ball_corridor_physics():
walker = jumping_ball.JumpingBallWithHead()
arena = corr_arenas.EmptyCorridor()
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(5, 0, 0),
walker_spawn_rotation=0,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
env = composer.Environment(time_limit=30,
task=task,
strip_singleton_obs_buffer_dim=True)
return walker, env
def jumping_ball_run_long():
walker = jumping_ball.JumpingBallWithHead()
arena = corr_arenas.EmptyCorridor(corridor_length=250,
visible_side_planes=False)
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(1, 0, 0),
walker_spawn_rotation=0,
contact_termination=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
strip_singleton_obs_buffer_dim=True)
def walker_run_long():
walker = planar_walker.PlanarWalker()
arena = corr_arenas.EmptyCorridor(corridor_length=250,
visible_side_planes=False)
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(1, 0, 0),
walker_spawn_rotation=0,
stand_height=1.2,
contact_termination=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
# (Chongyi Zheng): redefine reward function
task.get_reward = _walker_get_reward.__get__(task, task.get_reward)
return composer.Environment(time_limit=30,
task=task,
strip_singleton_obs_buffer_dim=True)
def jumping_ball_run_walls():
walker = jumping_ball.JumpingBallWithHead()
arena = corr_arenas.WallsCorridor(wall_gap=4.,
wall_width=distributions.Uniform(1, 7),
wall_height=3.0,
corridor_width=10,
corridor_length=250,
include_initial_padding=False)
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(0.5, 0, 0),
walker_spawn_rotation=0,
contact_termination=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
strip_singleton_obs_buffer_dim=True)
def ant_run_walls(random_state=None):
walker = ant.Ant()
arena = corr_arenas.WallsCorridor(wall_gap=4.,
wall_width=distributions.Uniform(1, 7),
wall_height=3.0,
corridor_width=10,
corridor_length=100,
include_initial_padding=False)
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(0.5, 0, 0),
walker_spawn_rotation=0,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
