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cgw_utils.py
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import os
import time
import numpy as np
import torch
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import roa_utils as utils
import cgw_sim_choi as choi
def to_np(x):
return x.detach().cpu().numpy()
def uniform(num_samples, xmin_xmax):
x = np.random.rand(num_samples)
xmin, xmax = xmin_xmax
return x * (xmax - xmin) + xmin
def dq_uniform(dqs, n):
d = len(dqs)
x = np.random.rand(n, d)
r = np.array(dqs)
x = (x - 0.5) * 2 * r
return x
def generate_traj_s(args):
# gait data
gait_data = args.gait_data
N = args.num_sampling
if args.evenly_q1:
q1 = uniform(N, (args.th1d, -args.th1d))
ref_q1 = choi.get_closest(q1, gait_data, dim=0)
ref_q2 = choi.get_closest(q1, gait_data, dim=1)
ref_q1d = choi.get_closest(q1, gait_data, dim=2)
ref_q2d = choi.get_closest(q1, gait_data, dim=3)
else:
idx = np.random.randint(low=0, high=gait_data.shape[0], size=(N,))
ref_q1 = gait_data[idx, 0]
ref_q2 = gait_data[idx, 1]
ref_q1d = gait_data[idx, 2]
ref_q2d = gait_data[idx, 3]
# add perturbation around
ref_s = np.stack((ref_q1, ref_q2, ref_q1d, ref_q2d), axis=-1)
noise_s = dq_uniform(args.radius_init, N)
init_s = ref_s + noise_s
return torch.from_numpy(init_s).float()
def pretrain_clf_from_uref(clf, actor, uref_func, data, args):
num_epochs, lr, print_freq, save_freq, viz_freq = args.pretrain_setups
num_epochs = int(num_epochs)
print_freq = int(print_freq)
save_freq = int(save_freq)
viz_freq = int(viz_freq)
train_zero_losses, train_dec_losses, train_losses = utils.get_n_meters(3)
optimizer = torch.optim.RMSprop(clf.parameters(), lr)
for epi in range(num_epochs):
#if epi == 0 or (args.regen_init and epi % args.regen_freq == 0 and epi != 0):
if epi % args.regen_freq == 0:
if args.traj_sampling:
sa_x = generate_traj_s(args)
else:
raise NotImplementedError
if args.gpus is not None:
sa_x = sa_x.cuda()
sa_u = uref_func(sa_x)
sa_x_curr = sa_x
sa_x_next = sa_x
for tti in range(args.num_sim_steps):
xdot = choi.compute_xdot(sa_x_next, sa_u, use_torch=True, args=args)
sa_x_next = sa_x_next + xdot * (args.dt / args.num_sim_steps)
sa_ref_zero = clf.get_ref(sa_x)
sa_loss_zero = clf(sa_ref_zero, sa_ref_zero) # (N, 1)
sa_v_curr = clf(sa_x_curr)
sa_v_next = clf(sa_x_next)
sa_loss_dec = torch.relu(sa_v_next - (1 - args.alpha_v) * sa_v_curr + args.v_thres) # (N, 1)
sa_loss_zero = args.zero_weight * torch.mean(sa_loss_zero)
sa_loss_dec = args.dec_weight * torch.mean(sa_loss_dec)
sa_loss = sa_loss_zero + sa_loss_dec
utils.update(train_zero_losses, sa_loss_zero)
utils.update(train_dec_losses, sa_loss_dec)
utils.update(train_losses, sa_loss)
sa_loss.backward()
optimizer.step()
optimizer.zero_grad()
if epi % print_freq == 0 or epi == num_epochs - 1:
print("PRET-CLF %05d/%05d L:%.3f (%.3f) Z:%.3f (%.3f) D:%.3f (%.3f)" % (
epi, num_epochs,
train_losses.val, train_losses.avg, train_zero_losses.val, train_zero_losses.avg,
train_dec_losses.val, train_dec_losses.avg))
if (not args.skip_viz) and epi % viz_freq == 0 or epi == num_epochs - 1:
plot_clf_heat(clf, epi, args, header="aprior_")
return clf
def pretrain_actor_from_uref(clf, actor, uref_func, data, args):
num_epochs, lr, print_freq, save_freq, viz_freq = args.pretrain_setups
num_epochs = int(num_epochs)
print_freq = int(print_freq)
save_freq = int(save_freq)
viz_freq = int(viz_freq)
train_losses, test_losses = utils.get_n_meters(2)
optimizer = torch.optim.RMSprop(actor.parameters(), lr)
for epi in range(num_epochs):
if epi % args.regen_freq == 0:
if args.traj_sampling:
sa_x = generate_traj_s(args)
else:
raise NotImplementedError
if args.gpus is not None:
sa_x = sa_x.cuda()
sa_u_ref = uref_func(sa_x)
sa_u_nn = actor(sa_x)
square_loss = torch.square(sa_u_ref - sa_u_nn)
num_train = int(square_loss.shape[0] * 0.8)
train_loss = torch.mean(square_loss[:num_train])
test_loss = torch.mean(square_loss[num_train:])
utils.update(train_losses, train_loss)
utils.update(test_losses, test_loss)
train_loss.backward()
optimizer.step()
optimizer.zero_grad()
if epi % print_freq == 0 or epi == num_epochs - 1:
print("PRET-U %05d/%05d train:%.3f (%.3f) test:%.3f (%.3f)" % (
epi, num_epochs, train_losses.val, train_losses.avg, test_losses.val, test_losses.avg))
return actor
def plot_sim(xs, xfs, ti, header, title, args, indices=None):
l = 1.0
offset = 0
doff = 3
left_end = -2
if args.test:
L = 5
ynL = 1
else:
L = 10
ynL = 1
for ii, idx in enumerate(indices):
off = offset + ii * doff
# slope
s_x0 = left_end
s_x1 = L * np.cos(args.alpha)
s_y0 = L * np.sin(args.alpha) + off
s_y1 = off
# bipedal
q1 = xs[idx, 0]
q2 = xs[idx, 1]
xf = xfs[idx]
q1x = xf * np.cos(args.alpha)
q1y = (L - xf) * np.sin(args.alpha)
cx = q1x - l * np.sin(q1 - args.alpha)
cy = q1y + l * np.cos(q1 - args.alpha)
q2x = cx + l * np.sin(q1 + q2 - args.alpha)
q2y = cy - l * np.cos(q1 + q2 - args.alpha)
plt.plot([s_x0, s_x1], [s_y0, s_y1], color="brown", label="slope" if ii == 0 else None)
plt.plot([q1x, cx], [q1y + off, cy + off], color="blue", label="leg-stance" if ii == 0 else None)
plt.plot([q2x, cx], [q2y + off, cy + off], color="red", label="leg-swing" if ii == 0 else None)
plt.scatter(q1x, q1y + off, color="red", label="Foot" if ii == 0 else None)
plt.scatter(cx, cy + off, color="black", label="CoM" if ii == 0 else None)
plt.legend()
plt.xlabel("x (m)")
plt.ylabel("y (m)")
plt.axis("scaled")
plt.xlim(left_end, L)
plt.ylim(0, ynL * L)
plt.title("SIM (t=%04d/%04d) %s"%(ti, args.nt, title))
plt.savefig("%s/%s_%04d.png"%(args.viz_dir, header, ti), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_examine(x_list_np, the_mask_np, s, gait_data, actor, args, epi):
f_list=[]
g_list=[]
x_list=[s]
u_list=[]
m_list=[]
x = s
for ti in range(args.nt):
prev_x = x
u = actor(x.detach())
for tti in range(args.num_sim_steps):
fvec = choi.get_fvec(x, use_torch=True)
gvec = choi.get_gvec(x, use_torch=True)
f_list.append(fvec)
g_list.append(gvec)
xdot = fvec + gvec * u
x = x + xdot * (args.dt / args.num_sim_steps)
if args.fine_switch:
x_mid = choi.compute_fine(x, prev_x, args)
x_plus = choi.compute_impact(x_mid, use_torch=True)
else:
x_plus = choi.compute_impact(x, use_torch=True)
mask = choi.detect_switch(x, prev_x, args)
x = x * (1 - mask) + x_plus * mask
x_list.append(x)
u_list.append(u)
m_list.append(mask)
f_list = torch.stack(f_list, dim=1)
g_list = torch.stack(g_list, dim=1)
x_list = torch.stack(x_list, dim=1)
u_list = torch.stack(u_list, dim=1)
m_list = torch.stack(m_list, dim=1)
f_np = to_np(f_list)
g_np = to_np(g_list)
x_np = to_np(x_list)
u_np = to_np(u_list)
m_np = to_np(m_list)
ind_list = [(0, 1), (1, 2), (2, 3), (1, 3)]
labels = ["q1 (rad)", "q2 (rad)", "q1d (rad/s)", "q2d (rad/s)"]
mins = args.exam_mins # [-0.5, -2.0, -4, -8.0]
maxs = args.exam_maxs # [0.5, 2.0, 4, 8.0]
os.makedirs("%s/debug/" % (args.viz_dir), exist_ok=True)
if args.colorful:
rand_val = np.random.random(args.num_samples)
# plot mag-increase plot
err_list=[]
for ti in range(args.nt):
q1_query_ful = x_np[:, ti, 0].reshape((-1, 1))
q1 = gait_data[:, 0].reshape((1, -1))
indices = np.argmin(np.abs(q1 - q1_query_ful), axis=1)
err_np = x_np[:, ti, :] - gait_data[indices, :]
err_list.append(err_np)
err_list = np.stack(err_list, axis=1)
is_inc_list=[]
for ti in range(1, args.nt):
is_inc = np.all(np.abs(err_list[:,ti,:]) > np.abs(err_list[:,ti-1,:]), axis=-1)
is_inc = np.logical_and(is_inc, m_np[:, ti, 0]==False)
is_inc_list.append(is_inc)
is_inc_list = np.stack(is_inc_list, axis=1)
is_inc_list = is_inc_list.astype(np.float32)
plt.figure(figsize=(8, 8))
for i in range(args.num_all_inc):
plt.subplot(args.num_all_inc + 1, 1, i+1)
plt.plot(range(args.nt-1), is_inc_list[i, :])
plt.xlabel("time step")
plt.ylabel("sample %02d - all inc"%(i))
plt.subplot(args.num_all_inc + 1, 1, args.num_all_inc + 1)
plt.plot(range(args.nt-1), np.mean(is_inc_list, axis=0))
plt.xlabel("time step")
plt.ylabel("increase rate")
plt.tight_layout()
plt.savefig("%s/debug/a_curve_e%05d.png" % (args.viz_dir, epi), bbox_inches='tight', pad_inches=0.1)
plt.close()
for ti in range(args.nt):
q1_query_ful = x_np[:, ti, 0].reshape((-1, 1))
q1 = gait_data[:, 0].reshape((1, -1))
indices = np.argmin(np.abs(q1 - q1_query_ful), axis=1)
err_np = x_np[:, ti, :] - gait_data[indices, :]
plt.figure(figsize=(10, 10))
fontsize = 20
markersize = 8
num_ticks = 5
for i in range(4):
plt.subplot(2, 2, i + 1)
plt.xticks(fontsize=fontsize)
plt.yticks(fontsize=fontsize)
idx0, idx1 = ind_list[i]
if args.elapse:
for exi in range(args.num_samples):
plt.plot(err_list[exi, :ti+1, idx0], err_list[exi, :ti+1, idx1], c="royalblue", alpha=0.5)
if args.colorful:
plt.scatter(err_np[:, idx0], err_np[:, idx1], s=markersize, c=rand_val)
else:
plt.scatter(err_np[:, idx0], err_np[:, idx1], s=markersize)
plt.scatter(0, 0, color="red", s=markersize * 3)
plt.xlim(mins[idx0], maxs[idx0])
plt.ylim(mins[idx1], maxs[idx1])
ax = plt.gca()
xmin, xmax = ax.get_xlim()
ax.set_xticks(np.round(np.linspace(xmin, xmax, num_ticks), 2))
ymin, ymax = ax.get_ylim()
ax.set_yticks(np.round(np.linspace(ymin, ymax, num_ticks), 2))
plt.xlabel(labels[idx0], fontsize=fontsize)
plt.ylabel(labels[idx1], fontsize=fontsize)
plt.suptitle("t=%03d/%03d" % (ti, args.nt), fontsize=fontsize)
plt.tight_layout()
plt.savefig("%s/debug/err_e%05d_t%03d.png" % (args.viz_dir, epi, ti), bbox_inches='tight', pad_inches=0.1)
plt.close()
os.system("convert -delay 10 -loop 0 %s/debug/err*0.png %s/debug/ani_e%05d.gif" % (args.viz_dir, args.viz_dir, epi))
for idx in range(args.num_samples):
if idx >= 20:
break
# idx = 0
print("idx=",idx)
q1_query_ful = x_np[idx,:-1,0].reshape((-1, 1))
q1 = gait_data[:, 0].reshape((1, -1))
indices = np.argmin(np.abs(q1 - q1_query_ful), axis=1)
plt.figure(figsize=(10, 10))
plt.subplot(12, 1, 1)
plt.plot(range(args.nt), f_np[idx, ::2, 0], label="f(x)_0", color="blue")
plt.legend()
plt.subplot(12, 1, 2)
plt.plot(range(args.nt), f_np[idx, ::2, 1], label="f(x)_1", color="blue")
plt.legend()
plt.subplot(12, 1, 3)
plt.plot(range(args.nt), f_np[idx, ::2, 2], label="f(x)_2", color="blue")
plt.legend()
plt.subplot(12, 1, 4)
plt.plot(range(args.nt), f_np[idx, ::2, 3], label="f(x)_3", color="blue")
plt.legend()
plt.subplot(12, 1, 5)
plt.plot(range(args.nt), g_np[idx, ::2, 2], label="g(x)_2", color="blue")
plt.legend()
plt.subplot(12, 1, 6)
plt.plot(range(args.nt), g_np[idx, ::2, 3], label="g(x)_3", color="blue")
plt.legend()
plt.subplot(12, 1, 7)
plt.plot(range(args.nt), g_np[idx, ::2, 2] * u_np[idx, :, 0], label="g(x)_2 u", color="blue")
plt.legend()
plt.subplot(12, 1, 8)
plt.plot(range(args.nt), g_np[idx, ::2, 3] * u_np[idx, :, 0], label="g(x)_3 u", color="blue")
plt.legend()
plt.subplot(12, 1, 9)
plt.plot(range(args.nt), x_np[idx, :-1, 0], label="x_0", color="blue")
plt.plot(range(args.nt), gait_data[indices, 0], label="gait x_0", color="black", linestyle="--")
plt.legend()
plt.subplot(12, 1, 10)
plt.plot(range(args.nt), x_np[idx, :-1, 1], label="x_1", color="blue")
plt.plot(range(args.nt), gait_data[indices, 1], label="gait x_1", color="black", linestyle="--")
plt.legend()
plt.subplot(12, 1, 11)
plt.plot(range(args.nt), x_np[idx, :-1, 2], label="x_2", color="blue")
plt.plot(range(args.nt), gait_data[indices, 2], label="gait x_2", color="black", linestyle="--")
plt.legend()
plt.subplot(12, 1, 12)
plt.plot(range(args.nt), x_np[idx, :-1, 3], label="x_3", color="blue")
plt.plot(range(args.nt), gait_data[indices, 3], label="gait x_3", color="black", linestyle="--")
plt.legend()
plt.tight_layout()
plt.savefig("%s/debug/e%05d_%04d.png" % (args.viz_dir, epi, idx), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_q1_others_curve(xs_np, gait_data, est_y_np, valids_np, ratio_val_np, args, epi):
os.makedirs("%s/q1" % (args.viz_dir), exist_ok=True)
xmin = [-0.2, -0.4, -1.25, -1.5]
xmax = [0.2, 0.4, 0.25, 2.5]
labels = ["q1 (rad)", "q2 (rad)", "q1d (rad/s)", "q2d (rad/s)"]
ind = [(0, 1), (0, 2), (0, 3), (2, 3)]
selected = ratio_val_np[:, 0]==0
for ti in range(args.nt):
if ti % args.print_sim_freq == 0:
s = 8
alpha=0.5
plt.figure(figsize=(8, 8))
for i in range(4):
plt.subplot(2, 2, i + 1)
idx0, idx1 = ind[i]
plt.plot(gait_data[:, idx0], gait_data[:, idx1], linestyle="--", color="black", alpha=0.5, label="gait")
if i == 3:
plt.plot(est_y_np[:, idx0 - 1], est_y_np[:, idx1 - 1], linestyle="-.", color="red", label="nn-est")
else:
plt.plot(gait_data[:, idx0], est_y_np[:, idx1 - 1], linestyle="-.", color="red", label="nn-est")
if args.elapse:
for exi in range(args.num_samples):
plt.plot(xs_np[exi, :ti+1, idx0], xs_np[exi, :ti+1, idx1], c="royalblue", alpha=0.5)
plt.scatter(xs_np[:, ti, idx0], xs_np[:, ti, idx1], color="blue", s=s, alpha=alpha)
if args.plot_valid_color:
plt.scatter(xs_np[selected, ti, idx0], xs_np[selected, ti, idx1], color="red", s=s, alpha=alpha)
plt.xlabel(labels[idx0])
plt.ylabel(labels[idx1])
plt.xlim(xmin[idx0], xmax[idx0])
plt.ylim(xmin[idx1], xmax[idx1])
plt.tight_layout()
plt.savefig("%s/q1/fit_e%05d_t%04d.png" % (args.viz_dir, epi, ti), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_traj_roa(x_curr, x_next, v_curr, v_next, args, epi):
mask = (v_next - v_curr) <= 0
gait_data = args.gait_data
ms = 8
# mask = mask
plt.figure(figsize=(8, 8))
plt.subplot(2, 2, 1)
plt.scatter(x_curr[np.where(mask==1), 0], x_curr[np.where(mask==1), 1], label="stable", color="blue", s=ms)
plt.scatter(x_curr[np.where(mask==0), 0], x_curr[np.where(mask==0), 1], label="unstable", color="red", s=ms)
plt.plot(gait_data[:, 0], gait_data[:, 1], color="black", linestyle="--", label="gait")
plt.legend()
plt.xlabel("q1 (rad)")
plt.ylabel("q2 (rad)")
plt.subplot(2, 2, 2)
plt.scatter(x_curr[np.where(mask == 1), 2], x_curr[np.where(mask == 1), 3], label="stable", color="blue", s=ms)
plt.scatter(x_curr[np.where(mask == 0), 2], x_curr[np.where(mask == 0), 3], label="unstable", color="red", s=ms)
plt.plot(gait_data[:, 2], gait_data[:, 3], color="black", linestyle="--", label="gait")
plt.legend()
plt.xlabel("q1d (rad/s)")
plt.ylabel("q2d (rad/s)")
plt.subplot(2, 2, 3)
max_plot=100
for i in range(min(x_curr.shape[0], 100)):
plt.plot([x_curr[i, 0], x_next[i, 0]], [x_curr[i, 1], x_next[i, 1]], color="blue")
plt.scatter(x_next[:max_plot, 0], x_next[:max_plot, 1], color="red", s=ms, marker="v")
plt.plot(gait_data[:, 0], gait_data[:, 1], color="black", linestyle="--", label="gait")
plt.xlabel("q1 (rad)")
plt.ylabel("q2 (rad)")
plt.subplot(2, 2, 4)
for i in range(min(x_curr.shape[0], 100)):
plt.plot([x_curr[i, 2], x_next[i, 2]], [x_curr[i, 3], x_next[i, 3]], color="blue")
plt.scatter(x_next[:max_plot, 2], x_next[:max_plot, 3], color="red", s=ms, marker="v")
plt.plot(gait_data[:, 2], gait_data[:, 3], color="black", linestyle="--", label="gait")
plt.xlabel("q1d (rad/s)")
plt.ylabel("q2d (rad/s)")
plt.tight_layout()
plt.savefig("%s/traj_roa_e%05d.png" % (args.viz_dir, epi), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_lya_curve(v_list_np, the_mask_np, epi, args):
assert the_mask_np.shape[0] == args.num_samples
for ni in range(args.num_samples):
if args.test or the_mask_np[ni, -1, 0] == 1:
if args.test:
NT = 300 + 1
else:
NT = args.nt + 1
plt.plot(range(NT), v_list_np[ni, :NT, 0], alpha=0.7)
plt.xlabel("Timestep")
plt.ylabel("CLF")
plt.savefig("%s/e%05d_curve.png" % (args.viz_dir, epi), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_phase(x_list_np, val_list_np, gait_data=None, epi=None, args=None, new_title=""):
xmin = -0.5
xmax = 0.5
ymin = -1.0
ymax = 1.0
draw_x = np.clip(x_list_np[:,:,0], xmin, xmax)
draw_y = np.clip(x_list_np[:,:,1], ymin, ymax)
xdmin = -1.0
xdmax = 1.0
ydmin = -2.0
ydmax = 2.0
draw_xd = np.clip(x_list_np[:, :, 2], xdmin, xdmax)
draw_yd = np.clip(x_list_np[:, :, 3], ydmin, ydmax)
for ti in range(args.nt):
if ti % args.print_sim_freq == 0:
plt.subplot(1, 2, 1)
if gait_data is not None:
plt.plot(gait_data[:,0], gait_data[:,1], color="black", linestyle="--", label="gait")
plt.plot(np.linspace(-0.5, 0, 100), np.linspace(1.0, 0.0, 100), color="pink", label="switching")
plt.scatter(draw_x[:, ti], draw_y[:, ti], s=1.0, alpha=0.3, color="blue")
plt.legend()
plt.axis("scaled")
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.xlabel("q1 (rad)")
plt.ylabel("q2 (rad)")
plt.title("Phase (t=%04d/%04d)" % (ti, args.nt))
plt.subplot(1, 2, 2)
if gait_data is not None:
plt.plot(gait_data[:, 2], gait_data[:, 3], color="black", linestyle="--", label="gait")
plt.scatter(draw_xd[:, ti], draw_yd[:, ti], s=1.0, alpha=0.3, color="blue")
plt.legend()
plt.axis("scaled")
plt.xlim(xdmin, xdmax)
plt.ylim(ydmin, ydmax)
plt.xlabel("q1d (rad/s)")
plt.ylabel("q2d (rad/s)")
# plt.title("Phase (t=%04d/%04d)" % (ti, args.nt))
plt.savefig("%s/e%05d_phase_%s%04d.png" % (args.viz_dir, epi, new_title, ti), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_phase_full(x_list_np, val_list_np, gait_data=None, epi=None, args=None, new_title=""):
x_mins = []
x_maxs = []
ind_list = [(0, 1), (0, 2), (0,3), (2,3)]
labels = ["q1 (rad)", "q2 (rad)", "q1d (rad/s)", "q2d (rad/s)"]
xmin = [-0.2, -0.4, -1.25, -1.5]
xmax = [0.2, 0.4, 0.25, 2.5]
for ti in range(args.nt):
if ti % 3 == 0:
plt.figure(figsize=(6, 6))
for i in range(4):
plt.subplot(2, 2, i+1)
ind1, ind2 = ind_list[i]
plt.plot(gait_data[:, ind1], gait_data[:, ind2], color="black", linestyle="--", label="gait")
plt.scatter(x_list_np[:, ti, ind1], x_list_np[:, ti, ind2], s=1.0, alpha=0.3, color="blue")
plt.legend()
# plt.axis("scaled")
plt.xlim(xmin[ind1], xmax[ind1])
plt.ylim(xmin[ind2], xmax[ind2])
plt.xlabel(labels[ind1])
plt.ylabel(labels[ind2])
plt.tight_layout()
plt.title("Phase (t=%04d/%04d)" % (ti, args.nt))
plt.savefig("%s/e%05d_phful_%s%04d.png" % (args.viz_dir, epi, new_title, ti), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_error_curve(x_list_np, epi, args):
NT = 300 + 1
NTS = range(NT)
q1 = x_list_np[0, :NT, 0]
q2 = x_list_np[0, :NT, 1]
q1d = x_list_np[0, :NT, 2]
q2d = x_list_np[0, :NT, 3]
q1_ref = q1
q2_ref = choi.poly(q1, args.params)
q1d_ref = q1d
q2d_ref = q2d
plt.subplot(4, 1, 1)
plt.plot(NTS, q1_ref, color="blue", label="q1_ref") # q1ref
plt.plot(NTS, q1, color="red", label="q1") # q1
plt.xlabel("timestep")
plt.ylabel("value")
plt.legend()
plt.subplot(4, 1, 2)
plt.plot(NTS, q2_ref, color="blue", label="q2_ref") # q1ref
plt.plot(NTS, q2, color="red", label="q2") # q1
plt.xlabel("timestep")
plt.ylabel("value")
plt.legend()
plt.subplot(4, 1, 3)
plt.plot(NTS, q1d_ref, color="blue", label="q1d_ref") # q1d_ref
plt.plot(NTS, q1d, color="red", label="q1d") # q1d
plt.xlabel("timestep")
plt.ylabel("value")
plt.legend()
plt.subplot(4, 1, 4)
plt.plot(NTS, q2d_ref, color="blue", label="q2d_ref") # q2d_ref
plt.plot(NTS, q2d, color="red", label="q2d") # q2d
plt.xlabel("timestep")
plt.ylabel("value")
plt.legend()
plt.savefig("%s/e%05d_err.png" % (args.viz_dir, epi), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_roa(x_list, the_mask, val_list, epi, args):
valids = np.all(val_list, axis=1).flatten()
plt.scatter(x_list[np.where(valids), 0, 0], x_list[np.where(valids), 0, 1], color="blue", s=1)
plt.scatter(x_list[np.where(valids == False), 0, 0], x_list[np.where(valids == False), 0, 1], color="red", s=1)
plt.xlabel("q1 (rad)")
plt.ylabel("q2 (rad)")
plt.savefig("%s/e%05d_roa.png" % (args.viz_dir, epi), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_real_roa(clf, actor, args, epi, oracle=None):
# dim (0, 1) | (1, 2) | (1, 3) | (2, 3)
# initialize (q1, q2, q1d, q2d)
# when choosing the rest 2 coords, the rest 2 will be the same equilibrium
N = args.num_samples
T = args.nt
ref_q = args.gait_data[0]
# generate the random
dq = np.random.rand(N, 4)
dq_vec = np.array(args.dq_init)
dq = (dq - 0.5) * 2 * dq_vec
s_rand = ref_q + dq
# (q1, q2) case
def task(ind, ind_keep, sub_i, num_t):
s = np.array(s_rand)
s[:, ind_keep[0]] = ref_q[ind_keep[0]]
s[:, ind_keep[1]] = ref_q[ind_keep[1]]
# simulate the trajectory
x = torch.from_numpy(s).float().cuda()
x_list = [x]
for ti in range(T):
prev_x = x
if args.opt_ctr:
u = choi.get_hjb_u(x, oracle)
else:
u = actor(x.detach())
# dynamics
for tti in range(args.num_sim_steps):
xdot = choi.compute_xdot(x, u, use_torch=True, args=args)
x = x + xdot * (args.dt / args.num_sim_steps)
# jump/reset
x_plus = choi.compute_impact(x, use_torch=True)
mask = choi.detect_switch(x, prev_x)
x = x * (1 - mask) + x_plus * mask
x_list.append(x)
x_list = torch.stack(x_list, dim=1)
# compute the V decreasing cases
v_list = clf(x_list.reshape(N*(T+1), -1)).reshape(N, T+1, 1)
x_list = x_list.detach().cpu()
# TODO(temp)
v_list = v_list[:, :num_t]
roa_mask = torch.all(v_list[:, 1:] < (1 - args.alpha) * v_list[:, :-1], dim=1)
roa_mask = roa_mask.float()
plt.subplot(2, 2, sub_i)
plt.scatter(x_list[torch.where(roa_mask!=1)[0], 0, ind[0]], x_list[torch.where(roa_mask!=1)[0], 0, ind[1]], color="red", label="unstable", s=1.0, alpha=0.5)
plt.scatter(x_list[torch.where(roa_mask)[0], 0, ind[0]], x_list[torch.where(roa_mask)[0], 0, ind[1]], color="blue", label="stable", s=1.0, alpha=0.5)
plt.xlabel(strs[ind[0]])
plt.ylabel(strs[ind[1]])
plt.legend()
plt.tight_layout()
strs = ["q1 (rad)", "q2 (rad)", "q1d (rad/s)", "q2d (rad/s)"]
for num_t in [2, 3, 5]:
task([0, 1], [2, 3], 1, num_t)
task([1, 2], [0, 3], 2, num_t)
task([1, 3], [0, 2], 3, num_t)
task([2, 3], [0, 1], 4, num_t)
plt.savefig("%s/e%05d_real_roa_T%d.png" % (args.viz_dir, epi, num_t), bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_curve(x_list_np, xf_list_np, u_list_np, v_list_np, gait_data, epi, args):
indices=[0, 1, 2, 3]
for idx in indices:
gait_q1 = choi.get_closest(x_list_np[idx, :, 0], gait_data, dim=0)
gait_q2 = choi.get_closest(x_list_np[idx, :, 0], gait_data, dim=1)
gait_q1d = choi.get_closest(x_list_np[idx, :, 0], gait_data, dim=2)
gait_q2d = choi.get_closest(x_list_np[idx, :, 0], gait_data, dim=3)
ref_q1 = args.params.th1d * np.ones_like(gait_q1)
ref_q2 = choi.poly(x_list_np[idx, :, 0], args.params)
ref_q1d = args.q1d_ref * np.ones_like(gait_q1d)
ref_q2d = choi.dpoly(x_list_np[idx, :, 0], x_list_np[idx, :, 2], args.params)
for ti in range(x_list_np.shape[1]):
if ti % (args.print_sim_freq) == 0:
fig = plt.figure(figsize=(10, 10))
gs = GridSpec(8, 1)
ts = range(ti)
alpha = 0.5
# q1
fig.add_subplot(gs[0, 0])
plt.plot(ts, x_list_np[idx, :ti, 0], color="red", label="sim")
plt.plot(ts, ref_q1[:ti], linestyle="-.", color="purple", label="track", alpha=alpha)
plt.plot(ts, gait_q1[:ti], linestyle="--", color="black", label="gait", alpha=alpha)
plt.xlim(0, args.nt+1)
plt.legend()
fig.add_subplot(gs[1, 0])
plt.plot(ts, x_list_np[idx, :ti, 1], color="red", label="sim")
plt.plot(ts, ref_q2[:ti], linestyle="-.", color="purple", label="track", alpha=alpha)
plt.plot(ts, gait_q2[:ti], linestyle="--", color="black", label="gait", alpha=alpha)
plt.xlim(0, args.nt + 1)
plt.legend()
fig.add_subplot(gs[2, 0])
plt.plot(ts, x_list_np[idx, :ti, 2], color="red", label="sim")
plt.plot(ts, ref_q1d[:ti], linestyle="-.", color="purple", label="track", alpha=alpha)
plt.plot(ts, gait_q1d[:ti], linestyle="--", color="black", label="gait", alpha=alpha)
plt.xlim(0, args.nt + 1)
plt.legend()
fig.add_subplot(gs[3, 0])
plt.plot(ts, x_list_np[idx, :ti, 3], color="red", label="sim")
plt.plot(ts, ref_q2d[:ti], linestyle="-.", color="purple", label="track", alpha=alpha)
plt.plot(ts, gait_q2d[:ti], linestyle="--", color="black", label="gait", alpha=alpha)
plt.xlim(0, args.nt + 1)
plt.legend()
fig.add_subplot(gs[4, 0])
if args.changed_dynamics:
plt.plot(ts, u_list_np[idx, :ti, 2], color="red", label="control")
else:
plt.plot(ts, u_list_np[idx, :ti, 0], color="red", label="control")
plt.xlim(0, args.nt + 1)
plt.legend()
fig.add_subplot(gs[5, 0])
plt.plot(ts, v_list_np[idx, :ti, 0], color="purple", label="lyapunov")
plt.xlim(0, args.nt + 1)
plt.legend()
fig.add_subplot(gs[6:, 0])
left_end = -2
off = 0
L = 5
l = 1
ynL = 1
# slope
s_x0 = left_end
s_x1 = L * np.cos(args.alpha)
s_y0 = L * np.sin(args.alpha) + off
s_y1 = off
# bipedal
q1 = x_list_np[idx, ti, 0]
q2 = x_list_np[idx, ti, 1]
xf = xf_list_np[idx, ti]
q1x = xf * np.cos(args.alpha)
q1y = (L - xf) * np.sin(args.alpha)
cx = q1x - l * np.sin(q1 - args.alpha)
cy = q1y + l * np.cos(q1 - args.alpha)
q2x = cx + l * np.sin(q1 + q2 - args.alpha)
q2y = cy - l * np.cos(q1 + q2 - args.alpha)
plt.plot([s_x0, s_x1], [s_y0, s_y1], color="brown", label="slope")
plt.plot([q1x, cx], [q1y + off, cy + off], color="blue", label="leg-stance")
plt.plot([q2x, cx], [q2y + off, cy + off], color="red", label="leg-swing")
plt.scatter(q1x, q1y + off, color="red", label="Foot")
plt.scatter(cx, cy + off, color="black", label="CoM")
plt.legend()
plt.xlabel("x (m)")
plt.ylabel("y (m)")
plt.axis("scaled")
plt.xlim(left_end, L)
plt.ylim(0, ynL * L)
plt.tight_layout()
plt.title("SIM (t=%04d/%04d)" % (ti, args.nt))
plt.savefig("%s/dbg_e%05d_i%04d_t%04d.png" % (args.viz_dir, epi, idx, ti),
bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_clf_heat(clf, epi, args, header=""):
for qi, q1 in enumerate([-0.13, 0, 0.13]):
plt.figure(figsize=(8, 8))
M = 100
err_d1, err_d2, err_d3 = args.clf_viz_err
err2 = torch.linspace(-err_d1, err_d1, M).cuda()
err3 = torch.linspace(-err_d2, err_d2, M).cuda()
err4 = torch.linspace(-err_d3, err_d3, M).cuda()
# err2 = torch.linspace(-0.5, 0.5, M).cuda()
# err3 = torch.linspace(-1, 1, M).cuda()
# err4 = torch.linspace(-1, 1, M).cuda()
err_23_x, err_23_y = torch.meshgrid(err2, err3)
err_24_x, err_24_y = torch.meshgrid(err2, err4)
err_34_x, err_34_y = torch.meshgrid(err3, err4)
err_23_x = err_23_x.T.reshape([-1, 1])
err_23_y = err_23_y.T.reshape([-1, 1])
err_24_x = err_24_x.T.reshape([-1, 1])
err_24_y = err_24_y.T.reshape([-1, 1])
err_34_x = err_34_x.T.reshape([-1, 1])
err_34_y = err_34_y.T.reshape([-1, 1])
zeros = torch.zeros_like(err_23_x)
q1_t = torch.ones_like(err_23_x) * q1
err1 = zeros
v_23 = clf.compute_v(q1_t, err1, err_23_x, err_23_y, zeros)
v_24 = clf.compute_v(q1_t, err1, err_24_x, zeros, err_24_y)
v_34 = clf.compute_v(q1_t, err1, zeros, err_34_x, err_34_y)
v_23 = to_np(v_23)
v_24 = to_np(v_24)
v_34 = to_np(v_34)
plt.subplot(2, 2, 1)
plt.imshow(v_23.reshape([M, M]), origin="lower")
plt.xlabel("q2_err")
plt.ylabel("q1d_err")
plt.colorbar()
plt.subplot(2, 2, 2)
plt.imshow(v_24.reshape([M, M]), origin="lower")
plt.xlabel("q2_err")
plt.ylabel("q2d_err")
plt.colorbar()
plt.subplot(2, 2, 3)
plt.imshow(v_34.reshape([M, M]), origin="lower")
plt.xlabel("q1d_err")
plt.ylabel("q2d_err")
plt.colorbar()
plt.savefig("%s/%sheat_e%05d_%02d_q1_%.4f.png" % (
args.viz_dir, header, epi, qi, q1), bbox_inches='tight', pad_inches=0.1)
plt.close()
def get_gait_data():
gait_str = """
[[ 0.1954769 -0.39095381 -0.75098276 0.84715623]
[ 0.18701912 -0.37403822 -0.75194013 0.73399985]
[ 0.17828843 -0.35657686 -0.73985833 0.61868256]
[ 0.16904408 -0.33808815 -0.73270839 0.53684717]
[ 0.15945166 -0.31890333 -0.72131479 0.46414152]
[ 0.14933339 -0.29866678 -0.70566869 0.40447736]
[ 0.14032325 -0.2806465 -0.68168187 0.35481673]
[ 0.13121651 -0.26243302 -0.66095102 0.30043977]
[ 0.12171151 -0.24342301 -0.63801199 0.24864183]
[ 0.11198891 -0.2239778 -0.61313689 0.19832826]
[ 0.1021308 -0.2042616 -0.58539337 0.15049179]
[ 0.09220324 -0.18440647 -0.55220789 0.10623652]
[ 0.08216057 -0.16432115 -0.51310581 0.06603827]
[ 0.07203654 -0.14407308 -0.46698153 0.03131433]
[ 0.06186418 -0.12372836 -0.41152471 0.00328764]
[ 0.05160491 -0.10320982 -0.34765154 -0.01696834]
[ 0.04122806 -0.08245611 -0.27609453 -0.02837917]]
"""
return utils.load_np_from_str(gait_str)
def get_around_init_gait_switch(args, perturbed=False):
gait_x = get_gait_data()
gait_x = torch.from_numpy(gait_x).float()
dx = utils.uniform_sample_tsr(args.num_samples, args.dx_mins, args.dx_maxs)
idx = np.random.choice(gait_x.shape[0], args.num_samples)
idx = torch.from_numpy(idx)
cfg = (torch.linspace(-0.20, -0.04, 17) * -1).reshape((-1, 1))[idx]
x = gait_x[idx] + dx
x[:, 1] = -2 * x[:, 0]
if perturbed:
cfg = cfg + utils.uniform_sample_tsr(args.num_samples, args.dcfg_min, args.dcfg_max)
cfg = torch.clamp(cfg, 0.04, 0.20)
return x, cfg
def get_initial_x(args):
if args.x_init_type == "whole":
x = utils.uniform_sample_tsr(args.num_samples, args.x_mins, args.x_maxs)
elif args.x_init_type == "switch":
x = utils.uniform_sample_tsr(args.num_samples, args.x_mins, args.x_maxs)
x[:, 1] = -2 * x[:, 0]
elif args.x_init_type == "around_init_gait":
gait_x = np.zeros((args.num_samples, 4))
dx = utils.uniform_sample_tsr(args.num_samples, args.dx_mins, args.dx_maxs)
x = gait_x + dx
elif args.x_init_type == "around_init_gait_switch":
x, cfg = get_around_init_gait_switch(args)
return x
def get_initial_u(args):
u = utils.uniform_sample_tsr(args.num_samples, args.u_mins, args.u_maxs)
return u
def get_u(x, u_init, args):
# u format (alpha, beta, theta, F1, F2)
# u = alpha * QP (theta) + beta * HJB + F1 * 1(if q2<0) + F2 * 1(if q2>=0)
# alpha, between (0, 1)
# beta, between (0, 1)
# theta, between (0.04, 0.20)
# u_f1, between (-1, 1)
# u_f2, between (-1, 1)
alpha, beta, theta, f1, f2 = torch.split(u_init, split_size_or_sections=1, dim=-1)
u_qp = choi.get_qp_u(x, args)
u_hjb = choi.get_hjb_u(x, args.oracle)
u_f1 = f1 * (x[:, 1:2] < 0).float()
u_f2 = f2 * (x[:, 1:2] >= 0).float()
u = (1 - beta) * alpha * u_qp + beta * u_hjb + u_f1 + u_f2
return u
def get_u_comp(x, alpha, beta, theta, f1, f2, args):
u_qp = choi.get_qp_u(x, args)
u_hjb = choi.get_hjb_u(x, args.oracle)
u_f1 = f1 * (x[:, 1:2] < 0).float()
u_f2 = f2 * (x[:, 1:2] >= 0).float()
u = (1 - beta) * alpha * u_qp + beta * u_hjb + u_f1 + u_f2
return u
def bipedal_step(x, u, xf, args):
l = 1
prev_x = x
for tti in range(args.num_sim_steps):
xdot = choi.compute_xdot(x, u, use_torch=False, args=args)
x = x + xdot * (args.dt / args.num_sim_steps)
if args.fine_switch:
x_mid = choi.compute_fine(x, prev_x, args, use_torch=False)
x_plus = choi.compute_impact(x_mid, use_torch=False)
mask = choi.detect_switch(x, prev_x, args, use_torch=False)
x = x * (1 - mask) + x_plus * mask
xf_plus = xf + l * np.sin(x_mid[:, 0:1] + x_mid[:, 1:2]) - l * np.sin(x_mid[:, 0:1])
xf = xf * (1 - mask) + xf_plus * mask
return x, xf, mask
def bipedal_next(x_init, u_init, args, obtain_full=False, xf=None,
use_rl=False, ppo_agent=None, agent=None, running=None, direct_u=None,
target_th=None, use_mbpo=False, use_pets=False):
l = 1
x = x_init.detach()
x_list = [x]
if obtain_full:
xf_list = [xf]
val_list = [torch.ones_like(x[:, 0:1])]
mask_list = [torch.zeros_like(x[:, 0:1])]
is_swi = torch.zeros_like(x[:, 0:1])
if any([use_rl, (direct_u is not None), use_mbpo, use_pets]):
args.params = choi.create_params()
else:
args.params = choi.create_params(th1d=-u_init[:, 2])
n_samples = x_init.shape[0]
flag_list = [None for _ in range(n_samples)]
x_seg=[[] for _ in range(n_samples)]
xf_seg=[[] for _ in range(n_samples)]
val_seg=[[] for _ in range(n_samples)]
# alpha, beta, theta, f1, f2 = torch.split(u_init, split_size_or_sections=1, dim=-1)
dbg_t1=time.time()
t_sim=0
if direct_u is not None:
nt = direct_u.shape[1] # (N, T, 1)
else:
nt = args.nt
for ti in range(nt):
prev_x = x
dbg_tmp = time.time()
if any([use_rl, use_mbpo, use_pets]):
if ppo_agent is not None:
rl_state = torch.cat((x, target_th), dim=-1)
u = ppo_agent.select_action(rl_state, use_torch=True, batch=True)
else:
the_x_th = torch.cat((x, target_th), dim=-1)
with torch.no_grad():
if use_rl:
rl_state = running(the_x_th, update=False, use_torch=True).float()