Big commit: Running and plotting thesis experiments

Signed-off-by: An Thai Le <an.thai.le97@gmail.com>
ASUDCT · Sep 19, 2021 · 4f042b6 · 4f042b6
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diff --git a/data/tasks/test/models/tphsmm_S2_5.p b/data/tasks/test/models/tphsmm_S2_5.p
diff --git a/data/tasks/test/models/tphsmm_S2_7.p b/data/tasks/test/models/tphsmm_S2_7.p
diff --git a/data/tasks/test/models/tphsmm_S2_9.p b/data/tasks/test/models/tphsmm_S2_9.p
diff --git a/data/tasks/test/models/tphsmm_S_11.p b/data/tasks/test/models/tphsmm_S_11.p
diff --git a/data/tasks/test/models/tphsmm_S_13.p b/data/tasks/test/models/tphsmm_S_13.p
diff --git a/data/tasks/test/models/tphsmm_S_15.p b/data/tasks/test/models/tphsmm_S_15.p
diff --git a/data/tasks/test/models/tphsmm_S_5.p b/data/tasks/test/models/tphsmm_S_5.p
diff --git a/data/tasks/test/models/tphsmm_S_7.p b/data/tasks/test/models/tphsmm_S_7.p
diff --git a/data/tasks/test/models/tphsmm_S_9.p b/data/tasks/test/models/tphsmm_S_9.p
diff --git a/data/tasks/test/models/tphsmm_U_11.p b/data/tasks/test/models/tphsmm_U_11.p
diff --git a/data/tasks/test/models/tphsmm_U_13.p b/data/tasks/test/models/tphsmm_U_13.p
diff --git a/data/tasks/test/models/tphsmm_U_15.p b/data/tasks/test/models/tphsmm_U_15.p
diff --git a/data/tasks/test/models/tphsmm_U_5.p b/data/tasks/test/models/tphsmm_U_5.p
diff --git a/data/tasks/test/models/tphsmm_U_7.p b/data/tasks/test/models/tphsmm_U_7.p
diff --git a/data/tasks/test/models/tphsmm_U_9.p b/data/tasks/test/models/tphsmm_U_9.p
diff --git a/data/tasks/test/models/tphsmm_hat1_11.p b/data/tasks/test/models/tphsmm_hat1_11.p
diff --git a/data/tasks/test/models/tphsmm_hat1_13.p b/data/tasks/test/models/tphsmm_hat1_13.p
diff --git a/data/tasks/test/models/tphsmm_hat1_15.p b/data/tasks/test/models/tphsmm_hat1_15.p
diff --git a/data/tasks/test/models/tphsmm_hat1_5.p b/data/tasks/test/models/tphsmm_hat1_5.p
diff --git a/data/tasks/test/models/tphsmm_hat1_7.p b/data/tasks/test/models/tphsmm_hat1_7.p
diff --git a/data/tasks/test/models/tphsmm_hat1_9.p b/data/tasks/test/models/tphsmm_hat1_9.p
diff --git a/data/tasks/test/models/tphsmm_hat_11.p b/data/tasks/test/models/tphsmm_hat_11.p
diff --git a/data/tasks/test/models/tphsmm_hat_13.p b/data/tasks/test/models/tphsmm_hat_13.p
diff --git a/data/tasks/test/models/tphsmm_hat_15.p b/data/tasks/test/models/tphsmm_hat_15.p
diff --git a/data/tasks/test/models/tphsmm_hat_5.p b/data/tasks/test/models/tphsmm_hat_5.p
diff --git a/data/tasks/test/models/tphsmm_hat_7.p b/data/tasks/test/models/tphsmm_hat_7.p
diff --git a/data/tasks/test/models/tphsmm_hat_9.p b/data/tasks/test/models/tphsmm_hat_9.p
diff --git a/data/tasks/test/models/tphsmm_test2.p b/data/tasks/test/models/tphsmm_test2.p
diff --git a/scripts/experiments/experiment_2d.py b/scripts/experiments/experiment_2d.py
@@ -0,0 +1,74 @@
+import sys
+import argparse
+from os.path import join, dirname, abspath
+import matplotlib
+import numpy as np
+import logging
+import time
+import pickle
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['font.size'] = 16
+
+ROOT_DIR = join(dirname(abspath(__file__)), '..', '..')
+sys.path.append(ROOT_DIR)
+from tprmp.utils.experiment import Experiment  # noqa
+
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+                                 description='Example run: python experiment.py 1')
+parser.add_argument('--mode', help='Load or not', type=int, default=1)
+args = parser.parse_args()
+
+task = 'test'
+demo_type = '2D'
+demo_names = ['C', 'C1', 'G', 'hat', 'hat1', 'I', 'I1', 'J', 'L', 'L1', 'P', 'S', 'S1', 'S2', 'U']
+start_random_radius = 0.01
+moving_goal_radius = 0.2
+disturb_magnitude = 10.
+disturb_period = [50, 150]
+disturb = True
+v_eps = 5e-2
+goal_eps = 0.2
+Kp = 1 * np.eye(2)
+Kd = 0.1 * np.eye(2)
+worker = Experiment(task=task, demo_type=demo_type, demo_names=demo_names, start_random_radius=start_random_radius, moving_goal_radius=moving_goal_radius,
+                    disturb_magnitude=disturb_magnitude, disturb_period=disturb_period, v_eps=v_eps, goal_eps=goal_eps)
+worker.load_demos()
+
+if args.mode == 1:
+    print('Start training models...')
+    worker.train()
+elif args.mode == 2:
+    print('Tracking experiment...')
+    error = worker.tracking_experiment()
+elif args.mode == 3:
+    print('Tracking baseline experiment...')
+    error = worker.tracking_baseline_experiment(Kp, Kd, dt=0.005)
+elif args.mode == 4:
+    print('Adaptation experiment...')
+    error = worker.adaptation_experiment(disturb=disturb)
+elif args.mode == 5:
+    print('Adaptation with increasing disturb force experiment...')
+    disturb = True
+    test_comps = [9]
+    worker.test_comps = test_comps
+    disturb_magnitude = 10. * np.arange(1, 10)
+    error = {}
+    for dm in disturb_magnitude:
+        worker.disturb_magnitude = dm
+        error[dm] = worker.adaptation_experiment(disturb=disturb)
+elif args.mode == 6:
+    print('Adaptation with increasing degree of difference experiment...')
+    disturb = False
+    test_comps = [9]
+    worker.test_comps = test_comps
+    moving_goal_radius = 0.1 * np.arange(1, 10)
+    error = {}
+    for r in moving_goal_radius:
+        worker.moving_goal_radius = r
+        error[r] = worker.adaptation_experiment(disturb=disturb)
+filename = join(worker.experiment_path, 'experiment_' + str(args.mode) + '_' + str(time.time()) + '.p')
+with open(filename, 'wb') as f:
+    pickle.dump(error, f)
diff --git a/scripts/experiments/experiment_6d.py b/scripts/experiments/experiment_6d.py
@@ -0,0 +1,90 @@
+import sys
+import argparse
+from os.path import join, dirname, abspath
+import matplotlib
+import numpy as np
+import logging
+import time
+import pickle
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['font.size'] = 16
+
+ROOT_DIR = join(dirname(abspath(__file__)), '..', '..')
+sys.path.append(ROOT_DIR)
+from tprmp.utils.experiment import Experiment  # noqa
+
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+                                 description='Example run: python experiment.py 1')
+parser.add_argument('--mode', help='Load or not', type=int, default=1)
+args = parser.parse_args()
+
+task = 'pick'
+demo_type = '6D'
+demo_names = ['sample']
+goal_frame = 'obj_frame'
+start_random_radius = 0.01
+omega = 30 * np.pi
+N = 10
+moving_goal_radius = 0.1
+disturb_magnitude = 10.
+disturb_period = [50, 150]
+disturb = False
+v_eps = 5e-2
+goal_eps = 0.2
+mass_scale = 0.2
+Kp = 1 * np.eye(6)
+Kd = 0.1 * np.eye(6)
+worker = Experiment(task=task, demo_type=demo_type, demo_names=demo_names, start_random_radius=start_random_radius, moving_goal_radius=moving_goal_radius,
+                    omega=omega, disturb_magnitude=disturb_magnitude, disturb_period=disturb_period, v_eps=v_eps, goal_eps=goal_eps, goal_frame=goal_frame)
+worker.load_demos()
+
+if args.mode == 1:
+    print('Start training models...')
+    worker.train()
+elif args.mode == 2:
+    print('Tracking experiment...')
+    error = worker.tracking_experiment()
+elif args.mode == 3:
+    print('Tracking baseline experiment...')
+    error = worker.tracking_baseline_experiment(Kp, Kd, dt=0.005)
+elif args.mode == 4:
+    print('Adaptation experiment...')
+    error = []
+    for n in range(N):
+        test_omega = omega + np.random.uniform(low=-5, high=5) * np.pi
+        worker.omega = test_omega
+        error.append(worker.adaptation_experiment(disturb=disturb))
+elif args.mode == 5:
+    print('Adaptation with increasing disturb force experiment...')
+    disturb = True
+    error = []
+    test_comps = [15]
+    worker.test_comps = test_comps
+    disturb_magnitude = 10. * np.arange(1, 50, 5)
+    for n in range(N):
+        test_omega = omega + np.random.uniform(low=-5, high=5) * np.pi
+        worker.omega = test_omega
+        error.append({})
+        for dm in disturb_magnitude:
+            worker.disturb_magnitude = dm
+            error[n][dm] = worker.adaptation_experiment(disturb=disturb)
+elif args.mode == 6:
+    print('Adaptation with increasing degree of difference experiment...')
+    disturb = False
+    test_comps = [15]
+    worker.test_comps = test_comps
+    moving_goal_radius = 0.1 * np.arange(1, 10)
+    error = []
+    for n in range(N):
+        test_omega = omega + np.random.uniform(low=-5, high=5) * np.pi
+        worker.omega = test_omega
+        error.append({})
+        for r in moving_goal_radius:
+            worker.moving_goal_radius = r
+            error[n][r] = worker.adaptation_experiment(disturb=disturb)
+filename = join(worker.experiment_path, 'experiment_' + str(args.mode) + '_' + str(time.time()) + '.p')
+with open(filename, 'wb') as f:
+    pickle.dump(error, f)
diff --git a/scripts/experiments/plot_appendix.py b/scripts/experiments/plot_appendix.py
@@ -0,0 +1,52 @@
+import sys
+from os.path import join, dirname, abspath
+import matplotlib
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['font.size'] = 16
+
+ROOT_DIR = join(dirname(abspath(__file__)), '..', '..')
+sys.path.append(ROOT_DIR)
+from tprmp.utils.loading import load_demos_2d  # noqa
+from tprmp.models.tp_rmp import TPRMP  # noqa
+from tprmp.visualization.dynamics import plot_potential_field, plot_dissipation_field  # noqa
+mode = 1
+task = 'test'
+demo_path = join(ROOT_DIR, 'data', 'tasks', task, 'demos')
+num_comp = 9
+N1, N2 = 3, 5
+limits = [0., 4.5]
+demo_names = ['C', 'C1', 'G', 'hat', 'hat1', 'I', 'I1', 'J', 'L', 'L1', 'P', 'S', 'S1', 'S2', 'U']
+dt = 0.01
+res = 0.05
+
+X, Y = np.meshgrid(np.arange(limits[0], limits[1], res), np.arange(limits[0], limits[1], res))
+fig, axs = plt.subplots(N1, N2)
+for n, name in enumerate(demo_names):
+    k, m = int(n / N2), n % N2
+    data_file = join(demo_path, name + '.p')
+    model_file = name + '_' + str(num_comp) + '.p'
+    demos = load_demos_2d(data_file, dt=dt)
+    model = TPRMP.load(task, model_name=model_file)
+    sample = demos[0]
+    frames = sample.get_task_parameters()
+    ax = axs[k, m]
+    plt.sca(ax)
+    ax.set_xlim([limits[0], limits[1]])
+    ax.set_ylim([limits[0], limits[1]])
+    if mode == 1:
+        plot_potential_field(model, frames, limits=limits, new_ax=False, res=res)
+    else:
+        plot_dissipation_field(model, frames, limits=limits, new_ax=False, res=res)
+
+for ax in fig.axes:
+    try:
+        ax.label_outer()
+    except:  # noqa
+        pass
+plt.show()
diff --git a/scripts/experiments/plot_explaining_K.py b/scripts/experiments/plot_explaining_K.py
@@ -0,0 +1,51 @@
+import sys
+from os.path import join, dirname, abspath
+import matplotlib
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['font.size'] = 16
+
+ROOT_DIR = join(dirname(abspath(__file__)), '..', '..')
+sys.path.append(ROOT_DIR)
+from tprmp.utils.loading import load_demos_2d  # noqa
+from tprmp.models.tp_rmp import TPRMP  # noqa
+from tprmp.visualization.demonstration import _plot_traj_global  # noqa
+from tprmp.visualization.models import _plot_gmm_global  # noqa
+
+dt = 0.01
+res = 0.05
+limits = [0., 4.5]
+task = 'test'
+demo_path = join(ROOT_DIR, 'data', 'tasks', task, 'demos')
+demo_name = 'S'
+data_file = join(demo_path, demo_name + '.p')
+num_comp = [5, 9, 15]
+
+fig, axs = plt.subplots(1, 3)
+X, Y = np.meshgrid(np.arange(limits[0], limits[1], res), np.arange(limits[0], limits[1], res))
+for k, n in enumerate(num_comp):
+    demos = load_demos_2d(data_file, dt=dt)
+    model_file = demo_name + '_' + str(n) + '.p'
+    model = TPRMP.load(task, model_name=model_file)
+    frames = demos[0].get_task_parameters()
+    ax = axs[k]
+    ax.set_aspect('equal')
+    plt.sca(ax)
+    _plot_traj_global(demos, title=f'{n} components', limits=limits, legend=False, three_d=False, new_ax=False)
+    _plot_gmm_global(model.model, frames, three_d=False, new_ax=False)
+    Z = np.zeros_like(X)
+    model.generate_global_gmm(frames)
+    for i in range(X.shape[0]):
+        for j in range(X.shape[1]):
+            Z[i, j] = model.compute_potential_field(np.array([X[i, j], Y[i, j]]))
+    c = ax.pcolormesh(X, Y, Z, cmap='RdBu', shading='auto', vmin=0., vmax=50, alpha=0.5)
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    fig.colorbar(c, cax=cax)
+plt.show()
diff --git a/scripts/experiments/plot_tracking_results.py b/scripts/experiments/plot_tracking_results.py
@@ -0,0 +1,96 @@
+import sys
+from os.path import join, dirname, abspath
+import matplotlib
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+matplotlib.rcParams['font.size'] = 16
+
+ROOT_DIR = join(dirname(abspath(__file__)), '..', '..')
+sys.path.append(ROOT_DIR)
+from tprmp.utils.loading import load_demos_2d  # noqa
+from tprmp.models.tp_rmp import TPRMP  # noqa
+from tprmp.demonstrations.base import Demonstration  # noqa
+from tprmp.visualization.demonstration import plot_frame_2d  # noqa
+from tprmp.demonstrations.manifold import Manifold  # noqa
+
+mode = 1
+task = 'test'
+demo_path = join(ROOT_DIR, 'data', 'tasks', task, 'demos')
+num_comp = 9
+N1, N2 = 3, 5
+limits = [0., 4.5]
+demo_names = ['C', 'C1', 'G', 'hat', 'hat1', 'I', 'I1', 'J', 'L', 'L1', 'P', 'S', 'S1', 'S2', 'U']
+dt = 0.01
+v_eps = 5e-2
+goal_eps = 0.2
+wait = 10
+res = 0.05
+colormap = 'RdBu' if mode == 1 else 'YlOrBr'
+manifold = Manifold.get_euclidean_manifold(2)
+
+
+def execute(model, frames, x0, dx0):
+    x, dx = x0, dx0
+    traj = [x]
+    model.generate_global_gmm(frames)
+    count = 0
+    while True:
+        ddx = model.retrieve(x, dx)
+        dx = ddx * dt + dx
+        x = manifold.exp_map(dx * dt, base=x)
+        traj.append(x)
+        if np.linalg.norm(dx) < v_eps:
+            count += 1
+            if count >= wait:
+                break
+        else:
+            count = 0
+    return np.array(traj).T
+
+
+X, Y = np.meshgrid(np.arange(limits[0], limits[1], res), np.arange(limits[0], limits[1], res))
+fig, axs = plt.subplots(N1, N2)
+for n, name in enumerate(demo_names):
+    k, m = int(n / N2), n % N2
+    data_file = join(demo_path, name + '.p')
+    if k == 2 and m == 0:
+        model_file = name + '_' + str(7) + '.p'
+    else:
+        model_file = name + '_' + str(num_comp) + '.p'
+    demos = load_demos_2d(data_file, dt=dt)
+    model = TPRMP.load(task, model_name=model_file)
+    sample = demos[0]
+    frames = sample.get_task_parameters()
+    x0, dx0 = sample.traj[:, 0], sample._d_traj[:, 0]
+    traj = execute(model, frames, x0, dx0)
+    ax = axs[k, m]
+    plt.sca(ax)
+    ax.set_aspect('equal')
+    ax.set_xlim([limits[0], limits[1]])
+    ax.set_ylim([limits[0], limits[1]])
+    ax.plot(sample.traj[0], sample.traj[1], color="b", linestyle='--', alpha=0.7)
+    ax.plot(traj[0], traj[1], color="b")
+    plot_frame_2d(frames.values())
+    Z = np.zeros_like(X)
+    for i in range(X.shape[0]):
+        for j in range(X.shape[1]):
+            if mode == 1:
+                Z[i, j] = model.compute_potential_field(np.array([X[i, j], Y[i, j]]))
+            else:
+                Z[i, j] = model.compute_dissipation_field(np.array([X[i, j], Y[i, j]]))
+    c = ax.pcolormesh(X, Y, Z, cmap=colormap, shading='auto', vmin=0., vmax=Z.max(), alpha=0.5)
+    fig.colorbar(c, ax=ax)
+    last = demos[0].traj[:, -1]
+    plt.scatter(last[0], last[1], marker='*', color='r', s=40)
+
+for ax in fig.axes:
+    try:
+        ax.label_outer()
+    except:  # noqa
+        pass
+plt.show()