def Simulate2dRamone(x0, duration,
desired_goal = 0.0,
print_period = 0.0):
builder = DiagramBuilder()
tree = RigidBodyTree()
AddModelInstanceFromUrdfFile("ramone_act.urdf", FloatingBaseType.kRollPitchYaw, None, tree)
plant = builder.AddSystem(RigidBodyPlant(tree))
controller = builder.AddSystem(
Ramone2dController(tree,
desired_goal=desired_goal,
print_period=print_period))
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), plant.get_input_port(0))
state_log = builder.AddSystem(SignalLogger(plant.get_num_states()))
builder.Connect(plant.get_output_port(0), state_log.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(True)
simulator.get_mutable_context().set_accuracy(1e-4)
state = simulator.get_mutable_context().get_mutable_continuous_state_vector()
state.SetFromVector(x0)
simulator.StepTo(duration)
return tree, controller, state_log, plant
def playbackMotion(data1, data2, data3, data4, times):
data = np.concatenate((data2, data1, data4, data3), axis=0)
tree = RigidBodyTree(
FindResource(
os.path.dirname(os.path.realpath(__file__)) +
"/block_pusher2.urdf"), FloatingBaseType.kFixed)
# Set up a block diagram with the robot (dynamics), the controller, and a
# visualization block.
builder = DiagramBuilder()
robot = builder.AddSystem(Player(data, times))
Tview = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]],
dtype=np.float64)
visualizer = builder.AddSystem(
PlanarRigidBodyVisualizer(tree,
Tview,
xlim=[-2.8, 4.8],
ylim=[-2.8, 10]))
#print(robot.get_output_port(0).size())
builder.Connect(robot.get_output_port(0), visualizer.get_input_port(0))
logger = builder.AddSystem(
SignalLogger(tree.get_num_positions() + tree.get_num_velocities()))
builder.Connect(robot.get_output_port(0), logger.get_input_port(0))
diagram = builder.Build()
# Set up a simulator to run this diagram
simulator = Simulator(diagram)
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(True)
# Simulate for 10 seconds
simulator.StepTo(times[-1] + 0.5)
def RunSimulation(quadrotor_plant,
control_law,
x0=np.random.random((8, 1)),
duration=30,
control_period=0.0333,
print_period=1.0,
simulation_period=0.0333):
quadrotor_controller = QuadrotorController(control_law,
control_period=control_period,
print_period=print_period)
# Create a simple block diagram containing the plant in feedback
# with the controller.
builder = DiagramBuilder()
# The last pendulum plant we made is now owned by a deleted
# system, so easiest path is for us to make a new one.
plant = builder.AddSystem(
QuadrotorPendulum(mb=quadrotor_plant.mb,
lb=quadrotor_plant.lb,
m1=quadrotor_plant.m1,
l1=quadrotor_plant.l1,
g=quadrotor_plant.g,
input_max=quadrotor_plant.input_max))
controller = builder.AddSystem(quadrotor_controller)
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), plant.get_input_port(0))
# Create a logger to capture the simulation of our plant
input_log = builder.AddSystem(SignalLogger(2))
input_log._DeclarePeriodicPublish(control_period, 0.0)
builder.Connect(controller.get_output_port(0), input_log.get_input_port(0))
state_log = builder.AddSystem(SignalLogger(8))
state_log._DeclarePeriodicPublish(control_period, 0.0)
builder.Connect(plant.get_output_port(0), state_log.get_input_port(0))
diagram = builder.Build()
# Set the initial conditions for the simulation.
context = diagram.CreateDefaultContext()
state = context.get_mutable_continuous_state_vector()
state.SetFromVector(x0)
# Create the simulator.
simulator = Simulator(diagram, context)
simulator.Initialize()
simulator.set_publish_every_time_step(True)
simulator.get_integrator().set_fixed_step_mode(True)
simulator.get_integrator().set_maximum_step_size(control_period)
# Simulate for the requested duration.
simulator.StepTo(duration)
return input_log, state_log
def simulateRobot(time, B, v_command):
tree = RigidBodyTree(
FindResource(
os.path.dirname(os.path.realpath(__file__)) +
"/block_pusher2.urdf"), FloatingBaseType.kFixed)
# Set up a block diagram with the robot (dynamics), the controller, and a
# visualization block.
builder = DiagramBuilder()
robot = builder.AddSystem(RigidBodyPlant(tree))
controller = builder.AddSystem(DController(tree, B, v_command))
builder.Connect(robot.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), robot.get_input_port(0))
Tview = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]],
dtype=np.float64)
visualizer = builder.AddSystem(
PlanarRigidBodyVisualizer(tree,
Tview,
xlim=[-2.8, 4.8],
ylim=[-2.8, 10]))
builder.Connect(robot.get_output_port(0), visualizer.get_input_port(0))
logger = builder.AddSystem(
SignalLogger(tree.get_num_positions() + tree.get_num_velocities()))
builder.Connect(robot.get_output_port(0), logger.get_input_port(0))
diagram = builder.Build()
# Set up a simulator to run this diagram
simulator = Simulator(diagram)
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(True)
# Set the initial conditions
context = simulator.get_mutable_context()
state = context.get_mutable_continuous_state_vector()
start1 = 3 * np.pi / 16
start2 = 15 * np.pi / 16
#np.pi/6 - eps, 2*np.pi/3 + eps, -np.pi/6 + eps, -2*np.pi/3 - eps, np.pi/6 - eps, 2*np.pi/3 + eps, -np.pi/6 + eps, -2*np.pi/3 - eps
state.SetFromVector(
(start1, start2, -start1, -start2, np.pi + start1, start2,
np.pi - start1, -start2, 1, 1, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0.)) # (theta1, theta2, theta1dot, theta2dot)
# Simulate for 10 seconds
simulator.StepTo(time)
#import pdb; pdb.set_trace()
return (logger.data()[8:11, :], logger.data()[:8, :],
logger.data()[19:22, :], logger.data()[11:19, :],
logger.sample_times())
def RunSimulation(pendulum_plant,
control_law,
x0=np.random.random((4, 1)),
duration=30):
pendulum_controller = PendulumController(control_law)
# Create a simple block diagram containing the plant in feedback
# with the controller.
builder = DiagramBuilder()
# The last pendulum plant we made is now owned by a deleted
# system, so easiest path is for us to make a new one.
plant = builder.AddSystem(
InertialWheelPendulum(m1=pendulum_plant.m1,
l1=pendulum_plant.l1,
m2=pendulum_plant.m2,
l2=pendulum_plant.l2,
r=pendulum_plant.r,
g=pendulum_plant.g,
input_max=pendulum_plant.input_max))
controller = builder.AddSystem(pendulum_controller)
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), plant.get_input_port(0))
# Create a logger to capture the simulation of our plant
input_log = builder.AddSystem(SignalLogger(1))
input_log._DeclarePeriodicPublish(0.033333, 0.0)
builder.Connect(controller.get_output_port(0), input_log.get_input_port(0))
state_log = builder.AddSystem(SignalLogger(4))
state_log._DeclarePeriodicPublish(0.033333, 0.0)
builder.Connect(plant.get_output_port(0), state_log.get_input_port(0))
diagram = builder.Build()
# Set the initial conditions for the simulation.
context = diagram.CreateDefaultContext()
state = context.get_mutable_continuous_state_vector()
state.SetFromVector(x0)
# Create the simulator.
simulator = Simulator(diagram, context)
simulator.Initialize()
simulator.set_publish_every_time_step(False)
simulator.get_integrator().set_fixed_step_mode(True)
simulator.get_integrator().set_maximum_step_size(0.005)
# Simulate for the requested duration.
simulator.StepTo(duration)
return input_log, state_log
def Simulate2dBallAndBeam(x0, duration):
builder = DiagramBuilder()
# Load in the ball and beam from a description file.
tree = RigidBodyTree()
AddModelInstancesFromSdfString(
open("ball_and_beam.sdf", 'r').read(), FloatingBaseType.kFixed, None,
tree)
# A RigidBodyPlant wraps a RigidBodyTree to allow
# forward dynamical simulation.
plant = builder.AddSystem(RigidBodyPlant(tree))
# Spawn a controller and hook it up
controller = builder.AddSystem(BallAndBeam2dController(tree))
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), plant.get_input_port(0))
# Create a logger to log at 30hz
state_log = builder.AddSystem(SignalLogger(plant.get_num_states()))
state_log._DeclarePeriodicPublish(0.0333, 0.0) # 30hz logging
builder.Connect(plant.get_output_port(0), state_log.get_input_port(0))
# Create a simulator
diagram = builder.Build()
simulator = Simulator(diagram)
# Don't limit realtime rate for this sim, since we
# produce a video to render it after simulating the whole thing.
#simulator.set_target_realtime_rate(100.0)
simulator.set_publish_every_time_step(False)
# Force the simulator to use a fixed-step integrator,
# which is much faster for this stiff system. (Due to the
# spring-model of collision, the default variable-timestep
# integrator will take very short steps. I've chosen the step
# size here to be fast while still being stable in most situations.)
integrator = simulator.get_mutable_integrator()
integrator.set_fixed_step_mode(True)
integrator.set_maximum_step_size(0.001)
# Set the initial state
state = simulator.get_mutable_context(
).get_mutable_continuous_state_vector()
state.SetFromVector(x0)
# Simulate!
simulator.StepTo(duration)
return tree, controller, state_log
def simulate_acrobot():
builder = DiagramBuilder()
acrobot = builder.AddSystem(AcrobotPlant())
saturation = builder.AddSystem(Saturation(min_value=[-10], max_value=[10]))
builder.Connect(saturation.get_output_port(0), acrobot.get_input_port(0))
wrapangles = WrapToSystem(4)
wrapangles.set_interval(0, 0, 2. * math.pi)
wrapangles.set_interval(1, -math.pi, math.pi)
wrapto = builder.AddSystem(wrapangles)
builder.Connect(acrobot.get_output_port(0), wrapto.get_input_port(0))
controller = builder.AddSystem(BalancingLQR())
builder.Connect(wrapto.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0),
saturation.get_input_port(0))
tree = RigidBodyTree(FindResource("acrobot/acrobot.urdf"),
FloatingBaseType.kFixed)
visualizer = builder.AddSystem(
PlanarRigidBodyVisualizer(tree, xlim=[-4., 4.], ylim=[-4., 4.]))
builder.Connect(acrobot.get_output_port(0), visualizer.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(False)
context = simulator.get_mutable_context()
state = context.get_mutable_continuous_state_vector()
parser = argparse.ArgumentParser()
parser.add_argument("-N",
"--trials",
type=int,
help="Number of trials to run.",
default=5)
parser.add_argument("-T",
"--duration",
type=float,
help="Duration to run each sim.",
default=4.0)
args = parser.parse_args()
print(AcrobotPlant)
for i in range(args.trials):
context.set_time(0.)
state.SetFromVector(UprightState().CopyToVector() +
0.05 * np.random.randn(4, ))
simulator.StepTo(args.duration)
def RunSimulation(self, real_time_rate=1.0):
'''
Here we test using the NNSystem in a Simulator to drive
an acrobot.
'''
sdf_file = "assets/acrobot.sdf"
urdf_file = "assets/acrobot.urdf"
builder = DiagramBuilder()
plant, scene_graph = AddMultibodyPlantSceneGraph(builder)
parser = Parser(plant=plant, scene_graph=scene_graph)
parser.AddModelFromFile(sdf_file)
plant.Finalize(scene_graph)
# Add
nn_system = NNSystem(self.pytorch_nn_object)
builder.AddSystem(nn_system)
# NN -> plant
builder.Connect(nn_system.NN_out_output_port,
plant.get_actuation_input_port())
# plant -> NN
builder.Connect(plant.get_continuous_state_output_port(),
nn_system.NN_in_input_port)
# Add meshcat visualizer
meshcat = MeshcatVisualizer(scene_graph)
builder.AddSystem(meshcat)
# builder.Connect(scene_graph.GetOutputPort("lcm_visualization"),
builder.Connect(scene_graph.get_pose_bundle_output_port(),
meshcat.GetInputPort("lcm_visualization"))
# build diagram
diagram = builder.Build()
meshcat.load()
# time.sleep(2.0)
RenderSystemWithGraphviz(diagram)
# construct simulator
simulator = Simulator(diagram)
# context = diagram.GetMutableSubsystemContext(
# self.station, simulator.get_mutable_context())
simulator.set_publish_every_time_step(False)
simulator.set_target_realtime_rate(real_time_rate)
simulator.Initialize()
sim_duration = 5.
simulator.StepTo(sim_duration)
print("stepping complete")
def animate_cartpole(policy, duration=10.):
# Animate the resulting policy.
builder = DiagramBuilder()
tree = RigidBodyTree("/opt/underactuated/src/cartpole/cartpole.urdf",
FloatingBaseType.kFixed)
plant = RigidBodyPlant(tree)
plant_system = builder.AddSystem(plant)
# TODO(russt): add wrap-around logic to barycentric mesh
# (so the policy has it, too)
class WrapTheta(VectorSystem):
def __init__(self):
VectorSystem.__init__(self, 4, 4)
def _DoCalcVectorOutput(self, context, input, state, output):
output[:] = input
twoPI = 2.*math.pi
output[1] = output[1] - twoPI * math.floor(output[1] / twoPI)
wrap = builder.AddSystem(WrapTheta())
builder.Connect(plant_system.get_output_port(0), wrap.get_input_port(0))
vi_policy = builder.AddSystem(policy)
builder.Connect(wrap.get_output_port(0), vi_policy.get_input_port(0))
builder.Connect(vi_policy.get_output_port(0), plant_system.get_input_port(0))
logger = builder.AddSystem(SignalLogger(4))
logger._DeclarePeriodicPublish(0.033333, 0.0)
builder.Connect(plant_system.get_output_port(0), logger.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_publish_every_time_step(False)
state = simulator.get_mutable_context().get_mutable_continuous_state_vector()
state.SetFromVector([-1., math.pi-1, 1., -1.])
# Do the sim.
simulator.StepTo(duration)
# Visualize the result as a video.
vis = PlanarRigidBodyVisualizer(tree, xlim=[-12.5, 12.5], ylim=[-1, 2.5])
ani = vis.animate(logger, repeat=True)
# plt.show()
# Things added to get visualizations in an ipynb
plt.close(vis.fig)
HTML(ani.to_html5_video())
def RunPendulumSimulation(pendulum_plant,
pendulum_controller,
x0=[0.9, 0.0],
duration=10):
'''
Accepts a pendulum_plant (which should be a
DampedOscillatingPendulumPlant) and simulates it for
'duration' seconds from initial state `x0`. Returns a
logger object which can return simulated timesteps `
logger.sample_times()` (N-by-1) and simulated states
`logger.data()` (2-by-N).
'''
# Create a simple block diagram containing the plant in feedback
# with the controller.
builder = DiagramBuilder()
plant = builder.AddSystem(pendulum_plant)
controller = builder.AddSystem(pendulum_controller)
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), plant.get_input_port(0))
# Create a logger to capture the simulation of our plant
# (We tell the logger to expect a 3-variable input,
# and hook it up to the pendulum plant's 3-variable output.)
logger = builder.AddSystem(SignalLogger(3))
logger.DeclarePeriodicPublish(0.033333, 0.0)
builder.Connect(plant.get_output_port(0), logger.get_input_port(0))
diagram = builder.Build()
# Create the simulator.
simulator = Simulator(diagram)
simulator.Initialize()
simulator.set_publish_every_time_step(False)
# Set the initial conditions for the simulation.
state = simulator.get_mutable_context().get_mutable_state()\
.get_mutable_continuous_state().get_mutable_vector()
state.SetFromVector(x0)
# Simulate for the requested duration.
simulator.AdvanceTo(duration)
# Return the logger, which stores the output of the
# plant across the time steps of the simulation.
return logger
def simulate(q0, qdot0, sim_time, controller):
# initialize block diagram
builder = DiagramBuilder()
# add system and controller
double_integrator = builder.AddSystem(get_double_integrator())
controller = builder.AddSystem(controller)
# wirw system and controller
builder.Connect(double_integrator.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), double_integrator.get_input_port(0))
# measure double-integrator state and input
state_logger = LogOutput(double_integrator.get_output_port(0), builder)
input_logger = LogOutput(controller.get_output_port(0), builder)
# finalize block diagram
diagram = builder.Build()
# instantiate simulator
simulator = Simulator(diagram)
simulator.set_publish_every_time_step(False) # makes sim faster
# set initial conditions
context = simulator.get_mutable_context()
context.SetContinuousState([q0, qdot0])
# run simulation
simulator.AdvanceTo(sim_time)
# unpack sim results
q_sim, qdot_sim = state_logger.data()
u_sim = input_logger.data().flatten()
t_sim = state_logger.sample_times()
return q_sim, qdot_sim, u_sim, t_sim
def getPredictedMotion(B, v_command, time):
#object_positions = object_positions + 0.1
#manipulator_positions = manipulator_positions + 0.1
#object_velocities = object_velocities + 0.1
#manipulator_velocities = manipulator_velocities + 0.1
#object_positions = object_positions[:, range(0,object_positions.shape[1],2)]
#manipulator_positions = manipulator_positions[:, range(0,manipulator_positions.shape[1],2)]
#object_velocities = object_velocities[:, range(0,object_velocities.shape[1],2)]
#manipulator_velocities = manipulator_velocities[:, range(0,manipulator_velocities.shape[1],2)]
#times = times[range(0,times.size,2)]
#import pdb; pdb.set_trace()
step = 0.01
A = 10 * np.eye(3)
tree = RigidBodyTree(
FindResource(
os.path.dirname(os.path.realpath(__file__)) +
"/block_pusher2.urdf"), FloatingBaseType.kFixed)
# Set up a block diagram with the robot (dynamics), the controller, and a
# visualization block.
builder = DiagramBuilder()
#robot = builder.AddSystem(RigidBodyPlant(tree))
robot = builder.AddSystem(
QuasiStaticRigidBodyPlant(tree, step, A, np.linalg.inv(B)))
controller = builder.AddSystem(QController(tree, v_command, step))
#builder.Connect(robot.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), robot.get_input_port(0))
Tview = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]],
dtype=np.float64)
visualizer = builder.AddSystem(
PlanarRigidBodyVisualizer(tree,
Tview,
xlim=[-2.8, 4.8],
ylim=[-2.8, 10]))
builder.Connect(robot.get_output_port(0), visualizer.get_input_port(0))
logger = builder.AddSystem(
SignalLogger(tree.get_num_positions() + tree.get_num_velocities()))
builder.Connect(robot.get_output_port(0), logger.get_input_port(0))
diagram = builder.Build()
# Set up a simulator to run this diagram
simulator = Simulator(diagram)
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(True)
# Set the initial conditions
context = simulator.get_mutable_context()
state = context.get_mutable_discrete_state_vector()
start1 = 3 * np.pi / 16
start2 = 15 * np.pi / 16
#np.pi/6 - eps, 2*np.pi/3 + eps, -np.pi/6 + eps, -2*np.pi/3 - eps, np.pi/6 - eps, 2*np.pi/3 + eps, -np.pi/6 + eps, -2*np.pi/3 - eps
state.SetFromVector(
(start1, start2, -start1, -start2, np.pi + start1, start2,
np.pi - start1, -start2, 1, 1, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0.)) # (theta1, theta2, theta1dot, theta2dot)
# Simulate for 10 seconds
simulator.StepTo(time)
#import pdb; pdb.set_trace()
return (logger.data()[8:11, :], logger.data()[:8, :],
logger.data()[19:22, :], logger.data()[11:19, :],
logger.sample_times())
builder = DiagramBuilder()
compass_gait = builder.AddSystem(CompassGait())
parser = argparse.ArgumentParser()
parser.add_argument("-T",
"--duration",
type=float,
help="Duration to run sim.",
default=10.0)
args = parser.parse_args()
logger = builder.AddSystem(SignalLogger(14))
builder.Connect(compass_gait.get_output_port(1), logger.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_publish_every_time_step(True)
simulator.get_mutable_context().SetAccuracy(1e-4)
state = simulator.get_mutable_context().get_mutable_continuous_state_vector()
state.SetFromVector([0., 0., 0.4, -2.])
simulator.StepTo(args.duration)
plt.figure()
plt.plot(logger.data()[4, :], logger.data()[11, :])
plt.xlabel('left leg angle')
plt.ylabel('left leg angular velocity')
plt.show()
class DrakeEnv(gym.Env):
metadata = {'render.modes': ['human']}
def __init__(self):
'''
Sets up the System diagram and creates a drake visualizer object to
send LCM messages to during the render method.
Subclasses must implement the methods below that throw a
NotImplementedError
'''
# Create the Diagram.
self.system = self.plant_system()
self.context = self.system.CreateDefaultContext()
# Create the simulator.
self.simulator = Simulator(self.system, self.context)
self.simulator.set_publish_every_time_step(False)
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def plant_system(self):
'''
Returns the fully constructed MDP diagram which is connected to the
vector source for simulation. Each subclass should implement this
method.
'''
raise NotImplementedError
def get_input_port_action(self):
'''
Returns the system input port that corresponds to the action
'''
raise NotImplementedError
def get_observation(self):
'''
Returns the system output port that corresponds to the observation
'''
raise NotImplementedError
def get_state(self):
'''
Returns the system output port that corresponds to the observation
'''
raise NotImplementedError
@property
def action_space(self):
'''
Specifies the limits of tha action space. This must be overridden by subclasses.
'''
raise NotImplementedError
@property
def observation_space(self):
'''
Specifies the limits of tha action space. This must be overridden by subclasses.
'''
raise NotImplementedError
def get_reward(self, state, action):
'''
Computes the reward from the state and an action
'''
raise NotImplementedError
def reset_state(self):
'''
Computes the reward from the state and an action
'''
raise NotImplementedError
def step(self, action):
'''
Simulates the system diagram for a short period of time
'''
# Only allow valid actions from the action space
action = validate_action(action, self.action_space)
# Fix input port with action and simulate
action_fixed_input_port_value = self.context.FixInputPort(
self.get_input_port_action().get_index(), action)
self.simulator.StepTo(self.context.get_time() + self.dt)
# Get the observation, reward, and terminal conditions
state = self.get_state()
observation = self.get_observation()
reward = self.get_reward(state, action)
done = self.is_done()
info = {}
return observation, reward, done, info
def reset(self):
'''
Resets the state in the system diagram
'''
self.context.set_time(0)
self.reset_state()
return self.get_observation()
def render(self, mode='human', close=False):
'''
Notifies the visualizer to draw the current state. Different implementations based on MultiBodyPlant and RigidBodyTree
'''
raise NotImplementedError
builder = DiagramBuilder()
acrobot = builder.AddSystem(RigidBodyPlant(tree))
parser = argparse.ArgumentParser()
parser.add_argument("-T", "--duration",
type=float,
help="Duration to run sim.",
default=10000.0)
args = parser.parse_args()
visualizer = builder.AddSystem(PlanarRigidBodyVisualizer(tree,
xlim=[-4., 4.],
ylim=[-4., 4.]))
builder.Connect(acrobot.get_output_port(0), visualizer.get_input_port(0))
ax = visualizer.fig.add_axes([.2, .95, .6, .025])
torque_system = builder.AddSystem(SliderSystem(ax, 'Torque', -5., 5.))
builder.Connect(torque_system.get_output_port(0),
acrobot.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(False)
context = simulator.get_mutable_context()
context.SetContinuousState([1., 0., 0., 0.])
simulator.StepTo(args.duration)
def SimulateHand(duration=10.,
num_fingers=3,
mu=0.5,
manipuland_sdf="models/manipuland_box.sdf",
initial_manipuland_pose=np.array([1.5, 0., 0.]),
n_grasp_search_iters=100,
manipuland_trajectory_callback=None,
control_period=0.0333,
print_period=1.0):
''' Given a great many passthrough arguments
(see docs for HandController and
usage example in set_5_mpc.ipynb), constructs
a simulation of a num_fingers-fingered hand
and simulates it for duration seconds from
a specified initial manipuland pose.
Returns:
(hand, plant, controller, state_log, contact_log)
hand: The RigidBodyTree of the complete hand.
plant: The RigidBodyPlant that owns the hand RBT
controller: The HandController object
state_log: A SignalLogger that has logged the output
of the state output port of plant.
contact_log: A PlanarHandContactLogger object that
has logged the output of the contact results output
port of the plant. '''
builder = DiagramBuilder()
tree = BuildHand(num_fingers, manipuland_sdf)
num_finger_links = 3 # from sdf
num_hand_q = num_finger_links * num_fingers
# Generate the nominal posture for the hand
# First link wide open, next links at right angles
x_nom = np.zeros(2 * num_finger_links * num_fingers + 6)
for i in range(num_fingers):
if i < num_fingers / 2:
x_nom[(num_finger_links*i):(num_finger_links*(i+1))] = \
np.array([2, 1.57, 1.57])
else:
x_nom[(num_finger_links*i):(num_finger_links*(i+1))] = \
-np.array([2, 1.57, 1.57])
# Drop in the initial manipuland location
x_nom[num_hand_q:(num_hand_q + 3)] = initial_manipuland_pose
# A RigidBodyPlant wraps a RigidBodyTree to allow
# forward dynamical simulation. It handles e.g. collision
# modeling.
plant = builder.AddSystem(RigidBodyPlant(tree))
# Alter the ground material used in simulation to make
# it dissipate more energy (to make the object less bouncy)
# and stickier (to make it easier to hold) and softer
# (again to make it less bouncy)
allmaterials = CompliantMaterial()
allmaterials.set_youngs_modulus(1E6) # default 1E9
allmaterials.set_dissipation(1.0) # default 0.32
allmaterials.set_friction(0.9) # default 0.9.
plant.set_default_compliant_material(allmaterials)
# Spawn a controller and hook it up
controller = builder.AddSystem(
HandController(
tree,
x_nom=x_nom,
num_fingers=num_fingers,
mu=mu,
n_grasp_search_iters=n_grasp_search_iters,
manipuland_trajectory_callback=manipuland_trajectory_callback,
control_period=control_period,
print_period=print_period))
nq = controller.nq
qinit, info = controller.ComputeTargetPosture(x_nom,
x_nom[(nq - 3):nq],
backoff_distance=0.0)
if info != 1:
print "Warning: initial condition IK solve returned info ", info
xinit = np.zeros(x_nom.shape)
xinit[0:(nq - 3)] = qinit[0:-3]
xinit[(nq - 3):nq] = x_nom[(nq - 3):nq]
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
for i in range(num_fingers):
builder.Connect(controller.get_output_port(i), plant.get_input_port(i))
# Create a state logger to log at 30hz
state_log = builder.AddSystem(SignalLogger(plant.get_num_states()))
state_log.DeclarePeriodicPublish(0.0333, 0.0) # 30hz logging
builder.Connect(plant.get_output_port(0), state_log.get_input_port(0))
# And a contact result logger, same rate
contact_log = builder.AddSystem(PlanarHandContactLogger(controller, plant))
contact_log.DeclarePeriodicPublish(0.0333, 0.0)
builder.Connect(plant.contact_results_output_port(),
contact_log.get_input_port(0))
# Create a simulator
diagram = builder.Build()
simulator = Simulator(diagram)
# Don't limit realtime rate for this sim, since we
# produce a video to render it after simulating the whole thing.
# simulator.set_target_realtime_rate(100.0)
simulator.set_publish_every_time_step(False)
# Force the simulator to use a fixed-step integrator,
# which is much faster for this stiff system. (Due to the
# spring-model of collision, the default variable-timestep
# integrator will take very short steps. I've chosen the step
# size here to be fast while still being stable in most situations.)
integrator = simulator.get_mutable_integrator()
integrator.set_fixed_step_mode(True)
integrator.set_maximum_step_size(0.005)
# Set the initial state
state = simulator.get_mutable_context().\
get_mutable_continuous_state_vector()
state.SetFromVector(xinit)
# Simulate!
simulator.StepTo(duration)
return tree, plant, controller, state_log, contact_log
compass_gait = builder.AddSystem(CompassGait())
parser = argparse.ArgumentParser()
parser.add_argument("-T", "--duration",
type=float,
help="Duration to run sim.",
default=10.0)
args = parser.parse_args()
hip_torque = builder.AddSystem(ConstantVectorSource([0.0]))
builder.Connect(hip_torque.get_output_port(0), compass_gait.get_input_port(0))
logger = builder.AddSystem(SignalLogger(14))
builder.Connect(compass_gait.get_output_port(1), logger.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_publish_every_time_step(True)
context = simulator.get_mutable_context()
context.SetAccuracy(1e-4)
context.SetContinuousState([0., 0., 0.4, -2.])
simulator.StepTo(args.duration)
plt.figure()
plt.plot(logger.data()[4, :], logger.data()[11, :])
plt.xlabel('left leg angle')
plt.ylabel('left leg angular velocity')
plt.show()
visualizer = builder.AddSystem(pbrv)
builder.Connect(rbplant_sys.get_output_port(0),
visualizer.get_input_port(0))
# And also log
signalLogRate = 60
signalLogger = builder.AddSystem(SignalLogger(nx))
signalLogger.DeclarePeriodicPublish(1. / signalLogRate, 0.0)
builder.Connect(rbplant_sys.get_output_port(0),
signalLogger.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.Initialize()
simulator.set_target_realtime_rate(1.0)
simulator.set_publish_every_time_step(False)
# TODO(russt): Clean up state vector access below.
state = simulator.get_mutable_context().get_mutable_state()\
.get_mutable_continuous_state().get_mutable_vector()
if set_initial_state:
initial_state = np.zeros((nx, 1))
initial_state[0] = 1.0
state.SetFromVector(initial_state)
simulator.StepTo(args.duration)
print(state.CopyToVector())
# Generate an animation of whatever happened
def Simulate2dHopper(x0,
duration,
desired_lateral_velocity=0.0,
print_period=0.0):
builder = DiagramBuilder()
# Load in the hopper from a description file.
# It's spawned with a fixed floating base because
# the robot description file includes the world as its
# root link -- it does this so that I can create a robot
# system with planar dynamics manually. (Drake doesn't have
# a planar floating base type accessible right now that I
# know about -- it only has 6DOF floating base types.)
tree = RigidBodyTree()
AddModelInstancesFromSdfString(
open("raibert_hopper_2d.sdf", 'r').read(), FloatingBaseType.kFixed,
None, tree)
# A RigidBodyPlant wraps a RigidBodyTree to allow
# forward dynamical simulation. It handles e.g. collision
# modeling.
plant = builder.AddSystem(RigidBodyPlant(tree))
# Alter the ground material used in simulation to make
# it dissipate more energy (to make the hopping more critical)
# and stickier (to make the hopper less likely to slip).
allmaterials = CompliantMaterial()
allmaterials.set_youngs_modulus(1E8) # default 1E9
allmaterials.set_dissipation(1.0) # default 0.32
allmaterials.set_friction(1.0) # default 0.9.
plant.set_default_compliant_material(allmaterials)
# Spawn a controller and hook it up
controller = builder.AddSystem(
Hopper2dController(tree,
desired_lateral_velocity=desired_lateral_velocity,
print_period=print_period))
builder.Connect(plant.get_output_port(0), controller.get_input_port(0))
builder.Connect(controller.get_output_port(0), plant.get_input_port(0))
# Create a logger to log at 30hz
state_log = builder.AddSystem(SignalLogger(plant.get_num_states()))
state_log._DeclarePeriodicPublish(0.0333, 0.0) # 30hz logging
builder.Connect(plant.get_output_port(0), state_log.get_input_port(0))
# Create a simulator
diagram = builder.Build()
simulator = Simulator(diagram)
# Don't limit realtime rate for this sim, since we
# produce a video to render it after simulating the whole thing.
#simulator.set_target_realtime_rate(100.0)
simulator.set_publish_every_time_step(False)
# Force the simulator to use a fixed-step integrator,
# which is much faster for this stiff system. (Due to the
# spring-model of collision, the default variable-timestep
# integrator will take very short steps. I've chosen the step
# size here to be fast while still being stable in most situations.)
integrator = simulator.get_mutable_integrator()
integrator.set_fixed_step_mode(True)
integrator.set_maximum_step_size(0.0005)
# Set the initial state
state = simulator.get_mutable_context(
).get_mutable_continuous_state_vector()
state.SetFromVector(x0)
# Simulate!
simulator.StepTo(duration)
return tree, controller, state_log
def simulate_and_log_policy_system(policy_system, expmt, ic=None):
global tree
global logger
expmt_settings = {
"pendulum": {
'constructor_or_path': PendulumPlant,
'state_dim': 2,
'twoPI_boundary_condition_state_idxs': (0,),
'initial_state': [0.1, 0.0],
},
"acrobot": {
'constructor_or_path': "/opt/underactuated/src/acrobot/acrobot.urdf",
'state_dim': 4,
'twoPI_boundary_condition_state_idxs': (0, 1),
'initial_state': [0.5, 0.5, 0.0, 0.0],
},
"cartpole": {
'constructor_or_path': "/opt/underactuated/src/cartpole/cartpole.urdf",
'state_dim': 4,
'twoPI_boundary_condition_state_idxs': (1,),
'initial_state': [0.5, 0.5, 0.0, 0.0],
},
}
assert expmt in expmt_settings.keys()
# Animate the resulting policy.
settings = expmt_settings[expmt]
builder = DiagramBuilder()
constructor_or_path = settings['constructor_or_path']
if not isinstance(constructor_or_path, str):
plant = constructor_or_path()
else:
tree = RigidBodyTree(constructor_or_path, FloatingBaseType.kFixed)
plant = RigidBodyPlant(tree)
plant_system = builder.AddSystem(plant)
# TODO(russt): add wrap-around logic to barycentric mesh
# (so the policy has it, too)
class WrapTheta(VectorSystem):
def __init__(self):
VectorSystem.__init__(self, settings['state_dim'], settings['state_dim'])
def _DoCalcVectorOutput(self, context, input, state, output):
output[:] = input
twoPI = 2.*math.pi
for idx in settings['twoPI_boundary_condition_state_idxs']:
# output[idx] += math.pi
output[idx] = output[idx]# - twoPI * math.floor(output[idx] / twoPI)
# output[idx] -= math.pi
wrap = builder.AddSystem(WrapTheta())
builder.Connect(plant_system.get_output_port(0), wrap.get_input_port(0))
vi_policy = builder.AddSystem(policy_system)
builder.Connect(wrap.get_output_port(0), vi_policy.get_input_port(0))
builder.Connect(vi_policy.get_output_port(0), plant_system.get_input_port(0))
x_logger = builder.AddSystem(SignalLogger(settings['state_dim']))
x_logger._DeclarePeriodicPublish(0.033333, 0.0)
builder.Connect(plant_system.get_output_port(0), x_logger.get_input_port(0))
u_logger = builder.AddSystem(SignalLogger(1))
u_logger._DeclarePeriodicPublish(0.033333, 0.0)
builder.Connect(vi_policy.get_output_port(0), u_logger.get_input_port(0))
diagram = builder.Build()
simulator = Simulator(diagram)
simulator.set_publish_every_time_step(False)
state = simulator.get_mutable_context().get_mutable_continuous_state_vector()
if ic is None:
ic = settings['initial_state']
state.SetFromVector(ic)
return simulator, tree, x_logger, u_logger
# wire cart-pole and lqr
builder.Connect(cartpole.get_state_output_port(), lqr.get_input_port(0))
builder.Connect(lqr.get_output_port(0), cartpole.get_actuation_input_port())
# add a visualizer and wire it
visualizer = builder.AddSystem(
PlanarSceneGraphVisualizer(scene_graph, xlim=[-3., 3.], ylim=[-1.2, 1.2], show=False)
)
builder.Connect(scene_graph.get_pose_bundle_output_port(), visualizer.get_input_port(0))
# finish building the block diagram
diagram = builder.Build()
# instantiate a simulator
simulator = Simulator(diagram)
simulator.set_publish_every_time_step(False) # makes sim faster
"""The following cell contains a function that simulates the closed-loop system and produces a video of the sim."""
# function that given the cart-pole initial state
# and the simulation time, simulates the system
# and produces a video
def simulate_and_animate(x0, sim_time=5):
# start recording the video for the animation of the simulation
visualizer.start_recording()
# reset initial time and state
context = simulator.get_mutable_context()
context.SetTime(0.)
context.SetContinuousState(x0)
