def __init__(self, Nrr=150., Izz=10., Kt1=0.1, zb=0., Mqq=100., ycp=0., xb=0., zcp=0., Yvv=100., yg=0., Ixx=10., Kt0=0.1, Xuu=1., xg=0., Zww=100., W=15.4*9.81, m=15.4, B=15.4*9.81, zg=0., Kpp=100., Qt1=-0.001, Qt0=0.001, Iyy=10., yb=0., xcp=0.1):
# become dynamics model
Dynamics.__init__(self)
# constant parameters
self.Nrr, self.Izz, self.Kt1, self.zb, self.Mqq, self.ycp, self.xb, self.zcp, self.Yvv, self.yg, self.Ixx, self.Kt0, self.Xuu, self.xg, self.Zww, self.W, self.m, self.B, self.zg, self.Kpp, self.Qt1, self.Qt0, self.Iyy, self.yb, self.xcp = Nrr, Izz, Kt1, zb, Mqq, ycp, xb, zcp, Yvv, yg, Ixx, Kt0, Xuu, xg, Zww, W, m, B, zg, Kpp, Qt1, Qt0, Iyy, yb, xcp
def body(a, beta):
def curr_energy(z, aux=None):
return (1 - beta) * init_energy(z) + (beta) * final_energy(z,
aux=aux)
last_x = a[1]
w = a[2]
v = a[3]
if refresh:
refreshed_v = v * tf.sqrt(1 - refreshment) + tf.random_normal(
tf.shape(v)) * tf.sqrt(refreshment)
else:
refreshed_v = tf.random_normal(tf.shape(v))
w = w + beta_diff * (- final_energy(last_x, aux=aux) \
+ init_energy(last_x, aux=aux))
dynamics = Dynamics(x_dim,
energy_function=curr_energy,
eps=step_size,
hmc=True,
T=leapfrogs)
Lx, Lv, px = dynamics.forward(last_x, aux=aux, init_v=refreshed_v)
mask = (px - tf.random_uniform(tf.shape(px)) >= 0.)
updated_x = tf.where(mask, Lx, last_x)
updated_v = tf.where(mask, Lv, -Lv)
return (px, updated_x, w, updated_v)
def stabSep(dyn):
"""
See [1] for implementation details
References:
[1] : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=78129&tag=1
"""
A_s, V, l = spl.schur(dyn.A, sort='lhp')
B_s = V.T @ dyn.B
C_s = dyn.C @ V
min_lmbda = np.min(npl.eigvals(A_s))
threshold = min_lmbda / 1e5
l = sum(npl.eigvals(A_s) < threshold) # remove close to unstable elements
# stable components
A_n = A_s[:l, :l]
B_n = B_s[:l, :]
C_n = C_s[:, :l]
# cross terms
A_c = A_s[:l, l:]
# unstable components
A_p = A_s[l:, l:]
B_p = B_s[l:, :]
C_p = C_s[:, l:]
# obtain stable and unstable dynamic systems
dyn_unstable = Dynamics(A_p, B_p, C_p)
B_tilde = np.hstack([B_n, A_c])
dyn_stable = Dynamics(A_n, B_tilde, C_n)
return dyn_stable, dyn_unstable, V, l
def __init__(self,
sess=None,
env_name=None,
feature_dim=None,
encoder_gamma=None,
encoder_hidden_size=None,
dynamics_hidden_size=None,
invdyn_hidden_size=None,
encoder_lr=None,
dynamics_lr=None,
invdyn_lr=None):
super(WrappedEnv, self).__init__()
self._sess = sess
self._env = gym.make(env_name)
self._state_dim = self._env.observation_space.shape[0]
self._action_dim = self._env.action_space.shape[0]
self._feature_dim = feature_dim
self._encoder_gamma = encoder_gamma
self._experience_buffer_size = 50000
self.observation_space = spaces.Box(
np.array([-np.inf] * self._feature_dim),
np.array([np.inf] * self._feature_dim))
self.action_space = self._env.action_space
self._num_hidden_layers = 2
self._encoder_hidden_sizes = [encoder_hidden_size
] * self._num_hidden_layers
self._dynamics_hidden_sizes = [dynamics_hidden_size
] * self._num_hidden_layers
self._invdyn_hidden_sizes = [invdyn_hidden_size
] * self._num_hidden_layers
self._encoder = Encoder(sess=self._sess,
input_dim=self._state_dim,
output_dim=feature_dim,
hidden_sizes=self._encoder_hidden_sizes,
learning_rate=encoder_lr)
self._dynamics = Dynamics(sess=self._sess,
state_dim=feature_dim,
action_dim=self._action_dim,
hidden_sizes=self._dynamics_hidden_sizes,
learning_rate=dynamics_lr)
self._inv_dynamics = InverseDynamics(
sess=self._sess,
state_dim=feature_dim,
action_dim=self._action_dim,
hidden_sizes=self._invdyn_hidden_sizes,
learning_rate=invdyn_lr)
self._state = self._env.reset()
def simulate(arm, traj, tf, dt_dyn, dt_control=None, u_inj=None):
"""
Inputs:
arm: arm object
traj: desired trajectory
tf: desired final simulation time
dt: desired dt for control
u_inj: control injection
"""
dynamics = Dynamics(arm, dt_dyn)
# dynamics = Dynamics(arm, dt_dyn, noise_torsion=[1e-3,0], noise_bending=[1e-3,0,0,0])
if dt_control is None:
dt_control = dt_dyn
else:
dt_control = max(np.floor(dt_control / dt_dyn) * dt_dyn, dt_dyn)
T = int(dt_control / dt_dyn)
dyn_reduced = Dynamics(arm, dt_control, n=arm.state.n)
controller = Controller(dyn_reduced)
state_list = []
control_list = []
t_steps = int(np.floor(tf / dt_dyn))
t_arr = np.linspace(0, tf, t_steps + 1)
for i, t in enumerate(t_arr):
print('Progress: {:.2f}%'.format(float(i) / (len(t_arr) - 1) * 100),
end='\r')
# mode = finiteStateMachine(y,wp)
# mode = 'none'
# mode = 'damping'
mode = 'mpc'
if i % T == 0:
j = i // T
if j not in u_inj:
wp = traj[:, j]
y = simulateMeasurements(arm)
u = controller.controlStep(y, wp, mode)
else:
u = {}
u['rot'] = u_inj[j]['rot']
u['lat'] = u_inj[j]['lat']
arm = dynamics.dynamicsStep(arm, u)
state_list.append(copy.copy(arm.state))
control_list.append(u)
return state_list, control_list, t_arr
def update(self,player_pos): # player_pos = positionof player = x,y player_x, player_y= player_pos[0] , player_pos[1] x1,y1 = self.position() new_pos = Dynamics(x1,y1,player_x,player_y,self.frame,self.rect) new_cen_x,new_cen_y = new_pos.near_move(self.maxstep) new_cen_x,new_cen_y = new_pos.sphere_move(new_cen_x,new_cen_y) new_x,new_y = new_cen_x - (self.rect.width/2.0) , new_cen_y - (self.rect.height/2.0) self.rect.topleft = new_x,new_y
def __init__(self, use_penalty=False, return_states=False):
self.dyn = Dynamics(dt=0.02)
self.Q = np.array([10, 10, 100, 20.5, 20.5, 20, 9.8, 9.8, 10])
self.Qf = self.Q
self.x0 = np.array([0, 0, -5., 0, 0, 0, 0, 0, 0])
self.x_des = self.x0.copy()
self.use_penalty = use_penalty
self.initialized = False
self.return_states = return_states
w_max = np.pi / 2
self.u_max = np.array([1, w_max, w_max, w_max])
self.u_min = np.array([0, -w_max, -w_max, -w_max])
self.mu = 10.
def __init__(self, model, noisemodel, state, breakpoints=None):
"""
May be used when noise is added to a problem, i. e. for stochastic
differential equations. All responsibility for handling the stochastics
is placed on a "noisemodel" function that is used together with the
derivative model normally used by the solver methods of the solvers
of the Dynamics and StiffDynamics classes.
"""
# First take care of what is inherited:
Dynamics.__init__(self, model, state, breakpoints)
# Then add the separate noise model
self.__noisemodel = noisemodel
def __init__(self, model, noisemodel, state, breakpoints=None):
"""
May be used when noise is added to a problem, i. e. for stochastic
differential equations. All responsibility for handling the stochastics
is placed on a "noisemodel" function that is used together with the
derivative model normally used by the solver methods of the solvers
of the Dynamics and StiffDynamics classes.
"""
# First take care of what is inherited:
Dynamics.__init__(self, model, state, breakpoints)
# Then add the separate noise model
self.__noisemodel = noisemodel
def main():
# instantiate dynamics
dyn = Dynamics()
# instantiate leg
leg = Leg(dyn, alpha=0, bound=True)
# set boudaries
t0 = 0
s0 = np.array([1000, 1000, *np.random.randn(4)], dtype=float)
l0 = np.random.randn(len(s0))
tf = 1000
sf = np.zeros(len(s0))
leg.set(t0, s0, l0, tf, sf)
# define problem
udp = Problem(leg, atol=1e-8, rtol=1e-8)
prob = pg.problem(udp)
# instantiate algorithm
uda = pg7.snopt7(True, "/usr/lib/libsnopt7_c.so")
uda.set_integer_option("Major iterations limit", 4000)
uda.set_integer_option("Iterations limit", 40000)
uda.set_numeric_option("Major optimality tolerance", 1e-2)
uda.set_numeric_option("Major feasibility tolerance", 1e-4)
algo = pg.algorithm(uda)
# instantiate population with one chromosome
pop = pg.population(prob, 1)
#pop = pg.population(prob, 0)
#pop.push_back([1000, *l0])
# optimise
pop = algo.evolve(pop)
def test_trajectory(traj):
ntrailers = traj['ntrailers']
t = traj['t']
x = traj['x']
y = traj['y']
phi = traj['phi']
theta = np.array(traj['theta'])
u1 = traj['u1']
u2 = traj['u2']
fu = make_interp_spline(t, np.array([u1, u2]).T, k=3)
dynamics = Dynamics(ntrailers)
def rhs(t, state):
u = fu(t)
r = dynamics.rhs(*state, *u)
r = np.reshape(r, (-1,))
return r
state0 = [x[0], y[0], phi[0]] + list(theta[:,0])
ans = solve_ivp(rhs, [t[0], t[-1]], state0, t_eval=t, max_step=1e-3)
plt.gca().set_prop_cycle(None)
plt.plot(ans.y[0], ans.y[1])
plt.gca().set_prop_cycle(None)
plt.plot(x, y, '--')
plt.show()
def main():
population = 6400 # Number of agents
average_degree = 4 # Average degree of network
num_episode = 1 # Number of episodes for taking ensemble average
network_type = 'lattice' # Topology of network
# Creating new directory for data files
new_dirname = "dyn_time_evolution_n" + str(population) + "e" + str(
num_episode) + "_" + network_type
os.mkdir(new_dirname)
os.chdir(new_dirname)
simulation = Dynamics(population, average_degree, network_type)
for episode in range(num_episode):
random.seed()
simulation.one_episode(episode)
def __init__(self, model, state, breakpoints=None):
"""
Basic initialization for the class. Cf. the Dynamics class for an
explanation of the input parameters.
"""
# First take care of what is inherited:
Dynamics.__init__(self, model, state, breakpoints)
# The explicit solvers presently only work for stacks, lists
# and 'd' arrays:
assert self._mode == 'list', \
"StiffDynamics only handles stacks, lists and 'd' arrays at the moment!"
# History array for the gearX solvers:
self.__prev = [] # For the gearX solvers
class CostFunctor:
def __init__(self, use_penalty=False, return_states=False):
self.dyn = Dynamics(dt=0.02)
self.Q = np.array([10, 10, 100, 20.5, 20.5, 20, 9.8, 9.8, 10])
self.Qf = self.Q
self.x0 = np.array([0, 0, -5., 0, 0, 0, 0, 0, 0])
self.x_des = self.x0.copy()
self.use_penalty = use_penalty
self.initialized = False
self.return_states = return_states
w_max = np.pi / 2
self.u_max = np.array([1, w_max, w_max, w_max])
self.u_min = np.array([0, -w_max, -w_max, -w_max])
self.mu = 10.
def __call__(self, z):
if z.shape[0] == z.size:
Tm = z.size
pop_size = 1
else:
pop_size, Tm = z.shape # default to use with nempc
horizon = Tm // 4
xk = np.tile(self.x0[:, None], pop_size)
cost = np.zeros(pop_size)
u_traj = z.reshape(pop_size, horizon, 4)
if self.return_states:
x_traj = []
for k in range(horizon):
xk = self.dyn.rk4(xk, u_traj[:, k].T)
x_err = xk - self.x_des[:, None]
if k < horizon - 1:
cost += np.sum(x_err**2 * self.Q[:, None], axis=0)
else:
cost += np.sum(x_err**2 * self.Qf[:, None], axis=0)
# penalty is only used to add cost for bound constraint violations
# when testing with gradient-based optimizer
penalty = 0
if self.use_penalty:
cost = cost.item()
if not self.initialized:
self.z_max = np.tile(self.u_max, horizon)
self.z_min = np.tile(self.u_min, horizon)
self.initialized = True
upper_bound = z - self.z_max
mask = upper_bound > 0
penalty += np.sum(upper_bound[mask]**2)
lower_bound = self.z_min - z
mask = lower_bound > 0
penalty += np.sum(lower_bound[mask]**2)
penalty *= self.mu / 2
if self.return_states:
x_traj = np.array(x_traj).flatten()
return x_traj, cost + penalty
return cost + penalty
def update(self,key): # key = a tuple # 1 = positive movement, -1 = negative movement, 0 = no movement y_move = key[0] x_move = key[1] x1,y1 = self.position() new_x_cen = x1 + (self.maxstep*x_move) new_y_cen = y1 + (self.maxstep*y_move) new_pos = Dynamics(new_x_cen,new_y_cen,x1,y1,self.dimensions,self.rect) new_x,new_y = new_x_cen - (self.rect.width/2.0) , new_y_cen - (self.rect.height/2.0) self.rect.topleft = new_pos.sphere_move(new_x,new_y)
def __init__(self, SCALE):
NOTES, FREQ_DICT = SCALE
self.note = Note(NOTES, FREQ_DICT)
self.rhythm = Rhythm()
self.dynamics = Dynamics()
self.equalizerFactor = {}
self.equalizerFactor[-0.1] = 1.0
self.equalizerFactor[220] = 1.0
self.equalizerFactor[440] = 0.7
self.equalizerFactor[880] = 0.35
self.equalizerFactor[1760] = 0.15
self.equalizerFactor[3520] = 0.15
self.equalizerFactor[7040] = 0.15
self.equalizerFactor[14080] = 0.15
self.equalizerFactor[28160] = 0.15
self.equalizerFactor[56320] = 0.15
self.equalizerBreakPoints = [
-0.1, 220, 440, 880, 1760, 3520, 7040, 14080, 28160, 56320
]
def __init__(self, config):
self.physical_params = config["physical_params"]
self.T = config["T"]
self.dt = config["dt"]
self.initial_state = config["xinit"]
self.goal_state = config["xgoal"]
self.ellipse_arc = config["ellipse_arc"]
self.deviation_cost = config["deviation_cost"]
self.Qf = config["Qf"]
self.limits = config["limits"]
self.n_state = 6
self.n_nominal_forces = 4
self.tire_model = config["tire_model"]
self.initial_guess_config = config["initial_guess_config"]
self.puddle_model = config["puddle_model"]
self.force_constraint = config["force_constraint"]
self.visualize_initial_guess = config["visualize_initial_guess"]
self.dynamics = Dynamics(self.physical_params.lf,
self.physical_params.lr,
self.physical_params.m,
self.physical_params.Iz, self.dt)
def get_hmc_samples(x_dim, eps, energy_function, sess, T=10, steps=200, samples=None):
hmc_dynamics = Dynamics(x_dim, energy_function, T=T, eps=eps, hmc=True)
hmc_x = tf.placeholder(tf.float32, shape=(None, x_dim))
Lx, _, px, hmc_MH = propose(hmc_x, hmc_dynamics, do_mh_step=True)
if samples is None:
samples = gaussian.get_samples(n=200)
final_samples = []
for t in range(steps):
final_samples.append(np.copy(samples))
Lx_, px_, samples = sess.run([Lx, px, hmc_MH[0]], {hmc_x: samples})
return np.array(final_samples)
def __init__(self, make_env, num_timesteps, envs_per_process):
self.make_env = make_env
self.envs_per_process = envs_per_process
self.num_timesteps = num_timesteps
self._set_env_vars()
self.policy = CnnPolicy(scope='cnn_pol',
ob_space=self.ob_space,
ac_space=self.ac_space,
hidsize=512,
feat_dim=512,
ob_mean=self.ob_mean,
ob_std=self.ob_std,
layernormalize=False,
nl=tf.nn.leaky_relu)
self.feature_extractor = InverseDynamics(policy=self.policy,
feat_dim=512,
layernormalize=0)
self.dynamics = Dynamics(auxiliary_task=self.feature_extractor,
mode=MODE,
feat_dim=512)
self.agent = PpoOptimizer(
scope='ppo',
ob_space=self.ob_space,
ac_space=self.ac_space,
policy=self.policy,
use_news=0,
gamma=.99,
lam=.98, #TODO Change this for potentially vastly different results
nepochs=3,
nminibatches=16,
lr=1e-4,
cliprange=.1, #TODO Change this as well
nsteps_per_seg=256,
nsegs_per_env=1,
ent_coeff=.001,
normrew=1,
normadv=1,
ext_coeff=0.,
int_coeff=1.,
dynamics=self.dynamics)
self.agent.to_report['aux'] = tf.reduce_mean(
self.feature_extractor.loss)
self.agent.total_loss += self.agent.to_report['aux']
self.agent.to_report['dyn_loss'] = tf.reduce_mean(self.dynamics.loss)
self.agent.total_loss += self.agent.to_report['dyn_loss']
self.agent.to_report['feat_var'] = tf.reduce_mean(
tf.nn.moments(self.feature_extractor.features, [0, 1])[1])
def __init__(self, SCALE):
NOTES, FREQ_DICT = SCALE
self.note = Note(NOTES, FREQ_DICT)
self.rhythm = Rhythm()
self.dynamics = Dynamics()
self.equalizerFactor = { }
self.equalizerFactor[-0.1] = 1.0
self.equalizerFactor[220] = 1.0
self.equalizerFactor[440] = 0.7
self.equalizerFactor[880] = 0.35
self.equalizerFactor[1760] = 0.15
self.equalizerFactor[3520] = 0.15
self.equalizerFactor[7040] = 0.15
self.equalizerFactor[14080] = 0.15
self.equalizerFactor[28160] = 0.15
self.equalizerFactor[56320] = 0.15
self.equalizerBreakPoints = [-0.1, 220, 440, 880, 1760, 3520, 7040, 14080, 28160, 56320]
def __init__(self, x0, xf):
Dynamics.__init__(self)
self.x0 = np.array(x0, float)
self.xf = np.array(xf, float)
def calculation(self):
start = self.start
goal = self.goal
theta, theta_future, displacement_rear, displacement_rear_future, steering_step = \
Dynamics(self.wheelbase, search_length=2.5, speed=5, dt=1)
# bz = Bezier(start, goal, self.start_heading, self.goal_heading, self.start_steering,
# self.mapsize, self.car_length, self.car_width, self.wheelbase, self.mapsize * 3, "NoFound")
# bezier_spline = bz.calculation()
x = start[0]
y = start[1]
x_future = x
y_future = y
x_prev = x
y_prev = y
heading_state = self.start_heading
rotate_angle = heading_state
steering_state = self.start_steering
found = 0
cost_list = [[0, [x, y], heading_state, steering_state]]
next_state = cost_list
path_discrete = list([]) # Initialize discrete path
global path
global path_tree
path = [] # Initialize continuous path
tree_leaf = [[x, y]] # Initialize search tree leaf (search failed)
search = 1
step = 1
path_tree = [] # Initialize continuous path for search trees
while found != 1:
if search >= self.freeGrid_num:
break
cost_list.sort(key=lambda x: x[0])
next_state = cost_list.pop(0)
path_discrete.append(np.round(next_state[1]))
path.append(next_state[1])
[x, y] = next_state[1]
[x_future, y_future] = [x, y]
heading_state = next_state[2]
steering_state = next_state[3]
if step > 1:
[x_prev, y_prev] = path[step - 1]
step += 1
rotate_angle = heading_state
if sqrt(
np.dot(np.subtract([x, y], goal), np.subtract(
[x, y], goal))) <= self.tolerance:
found = 1
rotate_matrix = [[np.cos(rotate_angle), -np.sin(rotate_angle)],
[np.sin(rotate_angle),
np.cos(rotate_angle)]]
action = (np.dot(displacement_rear, rotate_matrix)).tolist()
action_future = np.dot(displacement_rear_future, rotate_matrix)
candidates = np.add([x, y], action)
candidates_future = np.add([x_future, y_future], action_future)
candidates_round = np.round(candidates).astype(int)
heading_state = np.add(heading_state, theta)
invalid_ID = [
((candidates_round[i] == path).all(1).any() |
(candidates_round[i] == self.danger_zone).all(1).any() |
(candidates_round[i] == tree_leaf).all(1).any())
for i in range(len(candidates_round))
]
remove_ID = np.unique(
np.where((candidates < 0) | (candidates > self.mapsize))[0])
candidates = np.delete(candidates, remove_ID, axis=0)
candidates_future = np.delete(candidates_future, remove_ID, axis=0)
heading_state = np.delete(heading_state, remove_ID, axis=0)
candidates = np.delete(candidates, np.where(invalid_ID), axis=0)
candidates_future = np.delete(candidates_future,
np.where(invalid_ID),
axis=0)
heading_state = np.delete(heading_state,
np.where(invalid_ID),
axis=0)
if len(candidates) > 0:
cost_list = []
for i in range(len(candidates)):
diff = np.square(candidates[i] - self.bezier_spline)
min_dis = min(np.sqrt(np.sum(diff, axis=1)))
diff_future = np.square(candidates_future[i] -
self.bezier_spline)
min_dis_future = min(np.sqrt(np.sum(diff_future, axis=1)))
total_cost = min_dis + min_dis_future
cost_list.append([
total_cost, candidates[i], heading_state[i],
steering_step[i]
])
else:
search += 1
if (next_state[1] == tree_leaf).all(1).any():
tree_leaf.append(np.round([x_prev, y_prev]))
else:
tree_leaf.append((np.round([x, y])).tolist())
x = start[0]
y = start[1]
x_future = x
y_future = y
x_prev = x
y_prev = y
heading_state = self.start_heading
rotate_angle = heading_state
steering_state = self.start_steering
found = 0
cost_list = [[0, [x, y], heading_state, steering_state]]
next_state = cost_list
path_discrete = list([]) # Initialize discrete path
path_tree.append(path)
path = [] # Initialize continuous path
step = 1
bz_last = Bezier(next_state[1], goal, next_state[2], self.goal_heading,
self.start_steering, self.mapsize, self.car_length,
self.car_width, self.wheelbase, self.tolerance * 3,
"Found")
bz_last = bz_last.calculation()
path.append(bz_last)
return path, path_tree
from IPython.core.debugger import set_trace
from importlib import reload
# import sys
# sys.path.append("../")
import params as P
from dynamics import Dynamics
from animation import Animation
from plotData import plotData
from ekf_slam import EKF_SLAM
from copy import deepcopy
dynamics = Dynamics()
dataPlot = plotData()
ekf_slam = EKF_SLAM()
animation = Animation(ekf_slam)
estimate = np.array([])
actual = np.array([])
t = P.t_start
while t < P.t_end:
t_next_plot = t + P.t_plot
while t < t_next_plot:
t = t + P.Ts
vc = 1.25 + 0.5 * np.cos(2 * np.pi * (0.2) * t)
omegac = -0.5 + 0.2 * np.cos(2 * np.pi * (0.6) * t)
noise_v = vc + np.random.normal(
# arguments
n = 10
n_unstable = 3
n_r = 8
# Generate Dynamics Matrices
while True:
A = np.random.randn(n, n)
lmbda = npl.eig(A)[0]
if sum(lmbda >= 0) == n_unstable:
break
B = np.vstack([[1, 0], np.zeros([n - 2, 2]), [0, 1]])
dyn_f = Dynamics(A, B)
dyn_r = reduceDynamics(dyn_f, n_r, debug=0)
# Setup Simulation parameters
x0 = np.zeros(n)
x0[-1] = 5
tf = 2
dt = 0.01
t_arr = np.arange(0, tf, dt)
# simulate full dynamics
x_arr = np.zeros([n, len(t_arr)])
x = x0
for i, t in enumerate(t_arr):
dx = dyn_f.A @ x
def sample_dist(state, actions):
global hyperparameters
samples = []
dynamics = Dynamics()
# dynamics.fit(state,actions)
for traj_no in range(state.shape[0]):
# trajectory = 6
dynamics = Dynamics()
traj_states = state[traj_no, :, :]
traj_actions = actions[traj_no, :, :]
vals, acts = getPreviousSA(traj_no, traj_states, traj_actions)
T, dx = traj_states.shape
du = traj_actions.shape[1]
hyperparameters = {
'wx': [1 / float(dx) for i in range(dx)],
'wu': [1 / float(du) for i in range(du)]
}
eta = 1e-16
dynamics.fit(vals, acts)
prev_traj_dist = init_traj_dist(traj_states, traj_actions, dynamics,
hyperparameters)
traj_dist = prev_traj_dist
prev_mu, prev_sigma = forward(prev_traj_dist, dynamics)
prev_eta = -np.Inf
min_eta = prev_eta
_MAX_ITER = 5
for iter in range(_MAX_ITER):
# Collect samples in simulation
for sample in range(10):
s, a = get_sample(traj_dist, traj_no)
push_sample(traj_no, s, a)
vals, acts = getPreviousSA(traj_no, traj_states, traj_actions)
dynamics.fit(vals, acts, .01)
traj_dist, new_eta = backward(traj_states, traj_actions, dynamics,
eta, hyperparameters)
print(new_eta)
print('try again')
mu, sigma = forward(traj_dist, dynamics)
if new_eta > prev_eta:
min_eta = new_eta
# dynamics.fit(new_mu,new_sigma)
# # TODO: calculate KL divergence between new traj_dist and prev_traj_dist
# # check constraint, that kl_div <= _THRESHOLD
# kl_div = calculate_KL_div(mu, prev_mu, traj_dist, prev_traj_dist)
# print(kl_div)
# if kl_div <= _THRESHOLD:
# break
prev_traj_dist = traj_dist
#Take initial sample
samples = np.array([
-np.random.multivariate_normal(mu[t], sigma[t], 1).flatten()
for t in range(T)
])
commands = samples[:, 28:]
# raw_input()
f = open('trajectories/target/Traj{}pred.txt'.format(traj_no + 1), 'w')
print('here')
for act in commands:
f.write("{}\n".format(" ".join(str(x) for x in act.flatten())))
f.close()
return samples
import numpy as np
import params
from dynamics import Dynamics
from quadrotor_viewer import QuadRotor_Viewer
from scipy.spatial.transform import Rotation
from mpc import MPC, NMPC, LNMPC
from data_viewer import data_viewer
import time
if __name__ == "__main__":
dynamics = Dynamics(params.dt)
# viewer = QuadRotor_Viewer()
data_view = data_viewer()
A, B = dynamics.get_SS(dynamics.state)
#LNMPC doesn't seem to work any better
# controller = MPC(A, B, params.u_max, params.u_min, T=params.T) #Typicall MPC
controller = LNMPC(
A, B, params.u_max, params.u_min, T=params.T
) #Non-linear model predictive control relinearizing about the current state
# controller = NMPC(params.nu_max, params.nu_min, T = params.T) #Way to slow
t0 = params.t0
F_eq = params.mass * 9.81
T_eq = 0.0
u_eq = np.array([F_eq, T_eq, T_eq, T_eq])
xr = np.array([
5.0, -5.0, -5.0, 0.0, 0.0, 0.0, 0.0, 0.0,
np.deg2rad(0), 0.0, 0.0, 0.0
])
exec('from ' + sys.argv[1][:-3] + ' import HP, PM')
args = HP()
pm = PM()
print(vars_stat(pm))
data = Dataset(args.dtFilename, args.batchsize)
data_W = sum(1 for line in open(args.dtVocname) if line.rstrip())
model = LDA(data.n_tr, data_W, args.K, args.alpha, args.beta, args.sigma,
args.n_gsamp)
model.set_holdout_logperp(args.perpType, data.ho_train_cts,
data.ho_test_cts, args.n_window)
theta = args.beta + args.sigma * np.random.normal(size=(args.M, args.K,
data_W))
theta_tf = tf.Variable(theta)
grads_tf = tf.placeholder(dtype=theta.dtype, shape=theta.shape)
op_samples, dninfo = Dynamics(pm.dnType, pm).evolve(theta_tf,
L_grad_logp=grads_tf)
tr_times = []
with tf.Session() as sess:
tf.global_variables_initializer().run()
theta_smp, theta_par = zip(
*sess.run([dninfo.L_samples, dninfo.L_particles]))[0]
for i in range(args.n_round):
t_start = time.time()
for j in range(args.n_iter):
tr_train_cts, tr_test_cts = data.get_batch()
grads = model.get_grad_logp(tr_train_cts, theta=theta_par)
if j == args.n_iter - 1: break
theta_par = sess.run([op_samples, dninfo.L_particles],
{grads_tf: grads})[1][0]
theta_smp, theta_par = zip(
import sys
sys.path.append('..')
from cost import CostFunctor
from nempc import NEMPC
from dynamics import Dynamics
import numpy as np
import matplotlib.pyplot as plt
cost_fn = CostFunctor(use_penalty=False)
cost_fn.Q = np.array([10,10,100,2.5,2.5,2,1,1,1])
u_eq = np.array([0.5,0,0,0])
ctrl = NEMPC(cost_fn, 9, 4, cost_fn.u_min, cost_fn.u_max, u_eq, horizon=10,
population_size=500, num_parents=10, num_gens=200, mode='tournament',
warm_start=True)
dyn = Dynamics()
t = 0.0
tf = 10.0
ts = 0.02
x = np.array([0,0,-5.0,0,0,0,0,0,0])
x_des = np.array([0,1,-6.0,0,0,0,0,0,0])
state_hist = []
input_hist = []
time_hist = []
while t < tf:
print(t, end='\r')
time_hist.append(t)
state_hist.append(x)
class SFM(object):
# pitch registers
transpositionSemitones = 0
octaveNumber = 3
# tempo registers
tempo = 60
currentBeatValue = 60.0/tempo
currentBeat = 0
totalDuration = 0.0
# dynamics registers
crescendoSpeed = 1.1
currentCrescendoSpeed = crescendoSpeed
crescendoBeatsRemaining = 0.0
maximumAmplitude = 0.0
# tuple registers
duration = 60.0/tempo
amplitude = 1.0
decay = 1.0
# I/O
input = ""
#########################################################
# initialize Note, Rhythm, and Dynamics objects
def __init__(self, SCALE):
NOTES, FREQ_DICT = SCALE
self.note = Note(NOTES, FREQ_DICT)
self.rhythm = Rhythm()
self.dynamics = Dynamics()
self.equalizerFactor = { }
self.equalizerFactor[-0.1] = 1.0
self.equalizerFactor[220] = 1.0
self.equalizerFactor[440] = 0.7
self.equalizerFactor[880] = 0.35
self.equalizerFactor[1760] = 0.15
self.equalizerFactor[3520] = 0.15
self.equalizerFactor[7040] = 0.15
self.equalizerFactor[14080] = 0.15
self.equalizerFactor[28160] = 0.15
self.equalizerFactor[56320] = 0.15
self.equalizerBreakPoints = [-0.1, 220, 440, 880, 1760, 3520, 7040, 14080, 28160, 56320]
# self.tempo = 60
# self.currentBeatValue = 60.0/self.tempo
# self.octaveNumber = 3
def equalize(self, freq):
a, b = interval(freq, self.equalizerBreakPoints)
f = mapInterval(freq, a,b, self.equalizerFactor[a], self.equalizerFactor[b])
# print freq, a, b, f
return f
# return tuple as string given frequency,
# duration, decay, and amplitude
def _tuple(self, freq, duration):
output = `freq`
output += " "+`duration`
a = self.amplitude
a = a*self.equalize(freq)
output += " "+`a`
output += " "+`self.decay`
return output
# return tuple as string from frequency of token
# and root and suffix of token
def tuple(self, freq, root, suffix):
if suffix.find(",") > -1:
thisDuration = self.duration*(1 - self.rhythm.breath)
output = self._tuple(freq, thisDuration)
output += "\n"
output += self._tuple(0, self.duration - thisDuration)
else:
output = self._tuple(freq, self.duration)
return output
def updateRhythm(self, cmd):
self.currentBeatValue, self.duration = self.rhythm.value(cmd, self)
def emitNote(self, token):
if self.crescendoBeatsRemaining > 0:
self.amplitude = self.amplitude*self.currentCrescendoSpeed
self.crescendoBeatsRemaining -= self.currentBeatValue
freq, root, suffix = self.note.freq(token, self.transpositionSemitones, self.octaveNumber)
self.output += self.tuple(freq, root, suffix) + "\n"
# summary data
self.totalDuration += self.duration
self.currentBeat += self.currentBeatValue
if self.amplitude > self.maximumAmplitude:
self.maximumAmplitude = self.amplitude
def executeCommand(self, ops):
cmd = ops[0]
# if cmd is a rhythm symbol, change value of duration register
if self.rhythm.isRhythmOp(cmd):
self.updateRhythm(cmd)
# if cmd is a tempo command, change value of the tempo register
if self.rhythm.isTempoOp(cmd):
self.tempo = self.rhythm.tempo[cmd]
self.updateRhythm(cmd)
if cmd == "tempo":
self.tempo = float(ops[1])
self.updateRhythm(cmd)
# if cmd is an articulation command, change value of the decay register
if self.rhythm.isArticulationOp(cmd):
self.decay = self.rhythm.decay[cmd]
# if cmd is a dynamics command, change value of the amplitude register
if self.dynamics.isDynamicsConstant(cmd):
self.amplitude = self.dynamics.value[cmd]
# crescendo and decrescendo
if cmd == "crescendo" or cmd == "cresc":
self.crescendoBeatsRemaining = float(ops[1])
self.currentCrescendoSpeed = self.crescendoSpeed
if cmd == "decrescendo" or cmd == "decresc":
self.crescendoBeatsRemaining = float(ops[1])
self.currentCrescendoSpeed = 1.0/self.crescendoSpeed
# pitch transposition
if cmd == "octave":
self.octaveNumber = int(ops[1])
if cmd == "transpose":
self.transpositionSemitones = int(ops[1])
# pass special commands through
if cmd[0] == '@':
CMD = catList2(ops)
CMD = CMD[:len(CMD)]+"\n"
self.output += CMD
# tuples: returns a string of tuples from input = solfa text
def tuples(self):
# split intput into list of tokens
self.input = self.input.replace("\n", " ")
tokens = self.input.split(" ")
# make sure there are not empty list elements
tokens = filter( lambda x: len(x), tokens)
# initialize output
self.output = ""
for token in tokens:
if self.note.isNote(token):
self.emitNote(token)
else:
ops = token.split(":")
ops = filter(lambda x: len(x) > 0, ops)
if DEBUG == ON:
self.output += "cmd: "+ `ops`+"\n"
self.executeCommand(ops)
return self.output
class Optimization:
def __init__(self, config):
self.physical_params = config["physical_params"]
self.T = config["T"]
self.dt = config["dt"]
self.initial_state = config["xinit"]
self.goal_state = config["xgoal"]
self.ellipse_arc = config["ellipse_arc"]
self.deviation_cost = config["deviation_cost"]
self.Qf = config["Qf"]
self.limits = config["limits"]
self.n_state = 6
self.n_nominal_forces = 4
self.tire_model = config["tire_model"]
self.initial_guess_config = config["initial_guess_config"]
self.puddle_model = config["puddle_model"]
self.force_constraint = config["force_constraint"]
self.visualize_initial_guess = config["visualize_initial_guess"]
self.dynamics = Dynamics(self.physical_params.lf,
self.physical_params.lr,
self.physical_params.m,
self.physical_params.Iz, self.dt)
def build_program(self):
self.prog = MathematicalProgram()
# Declare variables.
state = self.prog.NewContinuousVariables(rows=self.T + 1,
cols=self.n_state,
name='state')
nominal_forces = self.prog.NewContinuousVariables(
rows=self.T, cols=self.n_nominal_forces, name='nominal_forces')
steers = self.prog.NewContinuousVariables(rows=self.T, name="steers")
slip_ratios = self.prog.NewContinuousVariables(rows=self.T,
cols=2,
name="slip_ratios")
self.state = state
self.nominal_forces = nominal_forces
self.steers = steers
self.slip_ratios = slip_ratios
# Set the initial state.
xinit = pack_state_vector(self.initial_state)
for i, s in enumerate(xinit):
self.prog.AddConstraint(state[0, i] == s)
# Constrain xdot to always be at least some value to prevent numerical issues with optimizer.
for i in range(self.T + 1):
s = unpack_state_vector(state[i])
self.prog.AddConstraint(s["xdot"] >= self.limits.min_xdot)
# Constrain slip ratio to be at least a certain magnitude.
if i != self.T:
self.prog.AddConstraint(
slip_ratios[i,
0]**2.0 >= self.limits.min_slip_ratio_mag**2.0)
self.prog.AddConstraint(
slip_ratios[i,
1]**2.0 >= self.limits.min_slip_ratio_mag**2.0)
# Control rate limits.
for i in range(self.T - 1):
ddelta = self.dt * (steers[i + 1] - steers[i])
f_dkappa = self.dt * (slip_ratios[i + 1, 0] - slip_ratios[i, 0])
r_dkappa = self.dt * (slip_ratios[i + 1, 1] - slip_ratios[i, 1])
self.prog.AddConstraint(ddelta <= self.limits.max_ddelta)
self.prog.AddConstraint(ddelta >= -self.limits.max_ddelta)
self.prog.AddConstraint(f_dkappa <= self.limits.max_dkappa)
self.prog.AddConstraint(f_dkappa >= -self.limits.max_dkappa)
self.prog.AddConstraint(r_dkappa <= self.limits.max_dkappa)
self.prog.AddConstraint(r_dkappa >= -self.limits.max_dkappa)
# Control value limits.
for i in range(self.T):
self.prog.AddConstraint(steers[i] <= self.limits.max_delta)
self.prog.AddConstraint(steers[i] >= -self.limits.max_delta)
# Add dynamics constraints by constraining residuals to be zero.
for i in range(self.T):
residuals = self.dynamics.nominal_dynamics(state[i], state[i + 1],
nominal_forces[i],
steers[i])
for r in residuals:
self.prog.AddConstraint(r == 0.0)
# Add the cost function.
self.add_cost(state)
# Add a different force constraint depending on the configuration.
if self.force_constraint == ForceConstraint.NO_PUDDLE:
self.add_no_puddle_force_constraints(
state, nominal_forces, steers,
self.tire_model.get_deterministic_model(), slip_ratios)
elif self.force_constraint == ForceConstraint.MEAN_CONSTRAINED:
self.add_mean_constrained()
elif self.force_constraint == ForceConstraint.CHANCE_CONSTRAINED:
self.add_chance_constraints()
else:
raise NotImplementedError("ForceConstraint type invalid.")
return
def constant_input_initial_guess(self, state, steers, slip_ratios,
nominal_forces):
# Guess the slip ratio as the minimum allowed value.
gslip_ratios = np.tile(
np.array([
self.limits.min_slip_ratio_mag, self.limits.min_slip_ratio_mag
]), (self.T, 1))
# Guess the slip angle as a linearly ramping steer that then becomes constant.
# This is done by creating an array of values corresponding to end_delta then
# filling in the initial ramp up phase.
gsteers = self.initial_guess_config["end_delta"] * np.ones(self.T)
igc = self.initial_guess_config
for i in range(igc["ramp_steps"]):
gsteers[i] = (i / igc["ramp_steps"]) * (igc["end_delta"] -
igc["start_delta"])
# Create empty arrays for state and forces.
gstate = np.empty((self.T + 1, self.n_state))
gstate[0] = pack_state_vector(self.initial_state)
gforces = np.empty((self.T, 4))
all_slips = self.T * [None]
# Simulate the dynamics for the initial guess differently depending on the force
# constraint being used.
if self.force_constraint == ForceConstraint.NO_PUDDLE:
tire_model = self.tire_model.get_deterministic_model()
for i in range(self.T):
s = unpack_state_vector(gstate[i])
# Simulate the dynamics for one time step.
gstate[i + 1], forces, slips = self.dynamics.simulate(
gstate[i], gsteers[i], gslip_ratios[i, 0],
gslip_ratios[i, 1], tire_model, self.dt)
# Store the results.
gforces[i] = pack_force_vector(forces)
all_slips[i] = slips
elif self.force_constraint == ForceConstraint.MEAN_CONSTRAINED or self.force_constraint == ForceConstraint.CHANCE_CONSTRAINED:
# mean model is a function that maps (slip_ratio, slip_angle, x, y) -> (E[Fx], E[Fy])
mean_model = self.tire_model.get_mean_model(
self.puddle_model.get_mean_fun())
for i in range(self.T):
# Update the tire model based off the conditions of the world
# at (x, y)
s = unpack_state_vector(gstate[i])
tire_model = lambda slip_ratio, slip_angle: mean_model(
slip_ratio, slip_angle, s["X"], s["Y"])
# Simulate the dynamics for one time step.
gstate[i + 1], forces, slips = self.dynamics.simulate(
gstate[i], gsteers[i], gslip_ratios[i, 0],
gslip_ratios[i, 1], tire_model, self.dt)
# Store the results.
gforces[i] = pack_force_vector(forces)
all_slips[i] = slips
else:
raise NotImplementedError(
"Invalid value for self.force_constraint")
# Declare array for the initial guess and set the values.
initial_guess = np.empty(self.prog.num_vars())
self.prog.SetDecisionVariableValueInVector(state, gstate,
initial_guess)
self.prog.SetDecisionVariableValueInVector(steers, gsteers,
initial_guess)
self.prog.SetDecisionVariableValueInVector(slip_ratios, gslip_ratios,
initial_guess)
self.prog.SetDecisionVariableValueInVector(nominal_forces, gforces,
initial_guess)
if self.visualize_initial_guess:
# TODO: reorganize visualizations
psis = gstate[:, 2]
xs = gstate[:, 4]
ys = gstate[:, 5]
fig, ax = plt.subplots()
if self.force_constraint != ForceConstraint.NO_PUDDLE:
plot_puddles(ax, self.puddle_model)
plot_ellipse_arc(ax, self.ellipse_arc)
plot_planned_trajectory(ax, xs, ys, psis, gsteers,
self.physical_params)
# Plot the slip ratios/angles
plot_slips(all_slips)
plot_forces(gforces)
if self.force_constraint == ForceConstraint.NO_PUDDLE:
generate_animation(xs,
ys,
psis,
gsteers,
self.physical_params,
self.dt,
puddle_model=None)
else:
generate_animation(xs,
ys,
psis,
gsteers,
self.physical_params,
self.dt,
puddle_model=self.puddle_model)
return initial_guess
def solve_program(self):
# Generate initial guess
initial_guess = self.constant_input_initial_guess(
self.state, self.steers, self.slip_ratios, self.nominal_forces)
# Solve the problem.
solver = SnoptSolver()
result = solver.Solve(self.prog, initial_guess)
solver_details = result.get_solver_details()
print("Exit flag: " + str(solver_details.info))
self.visualize_results(result)
def visualize_results(self, result):
state_res = result.GetSolution(self.state)
psis = state_res[:, 2]
xs = state_res[:, 4]
ys = state_res[:, 5]
steers = result.GetSolution(self.steers)
fig, ax = plt.subplots()
plot_ellipse_arc(ax, self.ellipse_arc)
plot_puddles(ax, self.puddle_model)
plot_planned_trajectory(ax, xs, ys, psis, steers, self.physical_params)
generate_animation(xs,
ys,
psis,
steers,
self.physical_params,
self.dt,
puddle_model=self.puddle_model)
plt.show()
def add_cost(self, state):
# Add the final state cost function.
diff_state = pack_state_vector(self.goal_state) - state[-1]
self.prog.AddQuadraticCost(diff_state.T @ self.Qf @ diff_state)
# Get the approx distance function for the ellipse.
fun = self.ellipse_arc.approx_dist_fun()
for i in range(self.T):
s = unpack_state_vector(state[i])
self.prog.AddCost(self.deviation_cost * fun(s["X"], s["Y"]))
def add_mean_constrained(self):
# mean model is a function that maps (slip_ratio, slip_angle, x, y) -> (E[Fx], E[Fy])
mean_model = self.tire_model.get_mean_model(
self.puddle_model.get_mean_fun())
for i in range(self.T):
# Get slip angles and slip ratios.
s = unpack_state_vector(self.state[i])
F = unpack_force_vector(self.nominal_forces[i])
# get the tire model at this position in space.
tire_model = lambda slip_ratio, slip_angle: mean_model(
slip_ratio, slip_angle, s["X"], s["Y"])
# Unpack values
delta = self.steers[i]
alpha_f, alpha_r = self.dynamics.slip_angles(
s["xdot"], s["ydot"], s["psidot"], delta)
kappa_f = self.slip_ratios[i, 0]
kappa_r = self.slip_ratios[i, 1]
# Compute expected forces.
E_Ffx, E_Ffy = tire_model(kappa_f, alpha_f)
E_Frx, E_Fry = tire_model(kappa_r, alpha_r)
# Constrain these expected force values to be equal to the nominal
# forces in the optimization.
self.prog.AddConstraint(E_Ffx - F["f_long"] == 0.0)
self.prog.AddConstraint(E_Ffy - F["f_lat"] == 0.0)
self.prog.AddConstraint(E_Frx - F["r_long"] == 0.0)
self.prog.AddConstraint(E_Fry - F["r_lat"] == 0.0)
def add_chance_constrained(self):
pass
def add_no_puddle_force_constraints(self, state, forces, steers,
pacejka_model, slip_ratios):
"""
Args:
prog:
state:
forces:
steers:
pacejka_model: function with signature (slip_ratio, slip_angle) using pydrake.math
"""
for i in range(self.T):
# Get slip angles and slip ratios.
s = unpack_state_vector(state[i])
F = unpack_force_vector(forces[i])
delta = steers[i]
alpha_f, alpha_r = self.dynamics.slip_angles(
s["xdot"], s["ydot"], s["psidot"], delta)
kappa_f = slip_ratios[i, 0]
kappa_r = slip_ratios[i, 1]
Ffx, Ffy = pacejka_model(kappa_f, alpha_f)
Frx, Fry = pacejka_model(kappa_r, alpha_r)
# Constrain the values from the pacejka model to be equal
# to the desired values in the optimization.
self.prog.AddConstraint(Ffx - F["f_long"] == 0.0)
self.prog.AddConstraint(Ffy - F["f_lat"] == 0.0)
self.prog.AddConstraint(Frx - F["r_long"] == 0.0)
self.prog.AddConstraint(Fry - F["r_lat"] == 0.0)
import hb_msgs.msg
import numpy as np
from hb_common.datatypes import Params, Command, ROSParameterException
from hb_common.helpers import saturate
from dynamics import Dynamics
# this is just the dynamic parameters from the ROS param server
params = Params._from_rosparam()
#this is artifically setting all our states and inputs to 0.5 to test our dynamics function
state = np.ones((6,1))*0.5
command = Command(left=0.5, right=0.5)
#making a dynamics object
d = Dynamics()
deriv_of_state = d.dynamics(params, state, command)
print("\n\n\n\nThis is the derivative of our state!!!")
print(deriv_of_state)
print("This is the derivative of our state!!!\n\n\n\n")
## for this input:
#state = np.ones((6,1))*0.5
#command = Command(left=0.5, right=0.5)
class WrappedEnv(gym.Env):
def __init__(self,
sess=None,
env_name=None,
feature_dim=None,
encoder_gamma=None,
encoder_hidden_size=None,
dynamics_hidden_size=None,
invdyn_hidden_size=None,
encoder_lr=None,
dynamics_lr=None,
invdyn_lr=None):
super(WrappedEnv, self).__init__()
self._sess = sess
self._env = gym.make(env_name)
self._state_dim = self._env.observation_space.shape[0]
self._action_dim = self._env.action_space.shape[0]
self._feature_dim = feature_dim
self._encoder_gamma = encoder_gamma
self._experience_buffer_size = 50000
self.observation_space = spaces.Box(
np.array([-np.inf] * self._feature_dim),
np.array([np.inf] * self._feature_dim))
self.action_space = self._env.action_space
self._num_hidden_layers = 2
self._encoder_hidden_sizes = [encoder_hidden_size
] * self._num_hidden_layers
self._dynamics_hidden_sizes = [dynamics_hidden_size
] * self._num_hidden_layers
self._invdyn_hidden_sizes = [invdyn_hidden_size
] * self._num_hidden_layers
self._encoder = Encoder(sess=self._sess,
input_dim=self._state_dim,
output_dim=feature_dim,
hidden_sizes=self._encoder_hidden_sizes,
learning_rate=encoder_lr)
self._dynamics = Dynamics(sess=self._sess,
state_dim=feature_dim,
action_dim=self._action_dim,
hidden_sizes=self._dynamics_hidden_sizes,
learning_rate=dynamics_lr)
self._inv_dynamics = InverseDynamics(
sess=self._sess,
state_dim=feature_dim,
action_dim=self._action_dim,
hidden_sizes=self._invdyn_hidden_sizes,
learning_rate=invdyn_lr)
self._state = self._env.reset()
def step(self, action):
next_state, reward, terminal, info = self._env.step(action)
encoded_next_state = self._encoder.predict(state=next_state)
batch_state = self._state
batch_action = action
batch_reward = reward
batch_next_state = next_state
batch_encoded_state = self._encoder.predict(state=batch_state)
batch_encoded_next_state = self._encoder.predict(
state=batch_next_state)
self._dynamics.update(state=batch_encoded_state,
action=batch_action,
next_state=batch_encoded_next_state)
self._inv_dynamics.update(state=batch_encoded_state,
action=batch_action,
next_state=batch_encoded_next_state)
dyn_gradients = self._dynamics.calc_gradients(
state=batch_encoded_state, action=action)
invdyn_gradients = self._inv_dynamics.calc_gradients(
state=batch_encoded_state, next_state=batch_encoded_next_state)
encoder_gradients = self._encoder.calc_gradients(state=batch_state)
dyn_state_gradients = dyn_gradients[:self._feature_dim]
dyn_action_gradients = dyn_gradients[self._feature_dim:]
invdyn_state_gradients = invdyn_gradients[:self._feature_dim]
invdyn_nstate_gradients = invdyn_gradients[self._feature_dim:]
encoder_optim_grads = np.dot(
invdyn_state_gradients,
invdyn_nstate_gradients) * dyn_state_gradients
self._encoder.update(state=batch_state,
optim_grads=encoder_optim_grads)
self._state = next_state
return encoded_next_state, reward, terminal, info
def reset(self):
self._state = self._env.reset()
encoded_state = self._encoder.predict(state=self._state)
return encoded_state
def render(self):
self._env.render()
two_link_robot_model_file = model_folder + 'two_link_robot_model.pkl'
# Create joint variables and define their relations
q0, q1, q2, q3, q4, q5, q6, q7, q8, q9 = new_sym('q:10')
# q3 = -q2 + q8
# q9 = -q8 + q2
robot_def = RobotDef([(0, -1, [1], 0, 0, 0, 0),
(1, 0, [2], 0, 0, -0.21537, q1),
(2, 1, [3], 0, -sympy.pi / 2, 0, q2 + sympy.pi / 2)],
dh_convention='mdh',
friction_type=['Coulomb', 'viscous', 'offset'])
geom = Geometry(robot_def)
dyn = Dynamics(robot_def, geom)
#if not os.path.exists(two_link_robot_model_file):
with open(two_link_robot_model_file, 'wr') as f:
pickle.dump(dyn.H_b, f)
H_b = None
if os.path.exists(two_link_robot_model_file):
with open(two_link_robot_model_file, 'rb') as f:
H_b = pickle.load(f)
print(H_b - dyn.H_b)
# import io, json
# with io.open('two_link_robot_model_file', 'w', encoding='utf-8') as f:
# f.write(json.dumps(data, ensure_ascii=False))
reload(dynamics)
from dynamics import Dynamics
import animation
reload(animation)
from animation import Animation
# import plotData
# reload(plotData)
# from plotData import plotData
import mcl
reload(mcl)
from mcl import MCL
dynamics = Dynamics()
animation = Animation()
# dataPlot = plotData()
mcl = MCL()
states = dynamics.states()
mu = mcl.get_mu()
std = mcl.get_std()
t = P.t_start
while t < P.t_end:
t_next_plot = t + P.t_plot
while t < t_next_plot:
t = t + P.Ts
if t >= 1:
mcl.change = True
# The Animation.py file is kept in the parent directory,
# so the parent directory path needs to be added.
sys.path.append('..')
from dynamics import Dynamics
from animation import Animation
t_start = 0.0 # Start time of simulation
t_end = 50.0 # End time of simulation
t_Ts = P.Ts # Simulation time step
t_elapse = 0.1 # Simulation time elapsed between each iteration
t_pause = 0.01 # Pause between each iteration
user_input = Sliders() # Instantiate Sliders class
simAnimation = Animation() # Instantiate Animate class
dynam = Dynamics() # Instantiate Dynamics class
t = t_start # Declare time variable to keep track of simulation time elapsed
while t < t_end:
plt.ion() # Make plots interactive
plt.figure(
user_input.fig.number) # Switch current figure to user_input figure
plt.pause(0.001) # Pause the simulation to detect user input
# The dynamics of the model will be propagated in time by t_elapse
# at intervals of t_Ts.
t_temp = t + t_elapse
while t < t_temp:
dynam.propagateDynamics( # Propagate the dynamics of the model in time
# so the parent directory path needs to be added.
sys.path.append('..')
from dynamics import Dynamics
from animation import Animation
t_start = 0.0 # Start time of simulation
t_end = 60.0 # End time of simulation
t_Ts = P.Ts # Simulation time step
t_elapse = 0.01 # Simulation time elapsed between each iteration
t_pause = 0.01 # Pause between each iteration
sig_gen = Signals() # Instantiate Signals class
plotGen = plotGenerator() # Instantiate plotGenerator class
# simAnimation = Animation() # Instantiate Animate class
ctrl = controllerSS()
dynam = Dynamics() # Instantiate Dynamics class
t = t_start # Declare time variable to keep track of simulation time elapsed
while t < t_end:
ref_input = sig_gen.getRefInputs(t)
# The dynamics of the model will be propagated in time by t_elapse
# at intervals of t_Ts.
t_temp = t + t_elapse
while t < t_temp:
states = dynam.States() # Get current states
u = ctrl.getForces(ref_input, states) # Calculate the forces
xhat = ctrl.getObsStates() # Get xhat