diff --git a/scripts/analyze_ball_cup_sticky_table_samplers.py b/scripts/analyze_ball_cup_sticky_table_samplers.py
new file mode 100644
index 0000000000..bbe0100d32
--- /dev/null
+++ b/scripts/analyze_ball_cup_sticky_table_samplers.py
@@ -0,0 +1,130 @@
+"""Analyze learned place-cup samplers for ball and cup sticky table."""
+
+import os
+from typing import Optional
+
+import dill as pkl
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import colormaps
+from matplotlib.colors import Normalize
+
+from predicators import utils
+from predicators.envs import BaseEnv, create_new_env
+from predicators.ground_truth_models import get_gt_options
+from predicators.settings import CFG
+from predicators.structs import Image, ParameterizedOption, State
+
+
+def _main() -> None:
+    """Loads the saved samplers."""
+    # Parse & validate args
+    args = utils.parse_args()
+    utils.update_config(args)
+    env = create_new_env(CFG.env, do_cache=True)
+    # Create an example state that includes the objects of interest. The actual
+    # state should not be used.
+    state = _create_example_state(env)
+    # Load the parameterized option of interest.
+    skill_name = "PlaceCupWithoutBallOnTable"
+    options = get_gt_options(env.get_name())
+    option = next(o for o in options if o.name == skill_name)
+    # Set up videos.
+    video_frames = []
+    # Evaluate samplers for each learning cycle.
+    for online_learning_cycle in range(CFG.num_online_learning_cycles):
+        img = _run_one_cycle_analysis(online_learning_cycle, option)
+        video_frames.append(img)
+    # Save the videos.
+    video_outfile = "place_on_table_sampler_learning.mp4"
+    utils.save_video(video_outfile, video_frames)
+
+
+def _run_one_cycle_analysis(online_learning_cycle: Optional[int],
+                            param_option: ParameterizedOption) -> Image:
+    option_name = param_option.name
+    approach_save_path = utils.get_approach_save_path_str()
+    save_path = f"{approach_save_path}_{option_name}_{online_learning_cycle}"
+    assert CFG.active_sampler_learning_object_specific_samplers
+    suffix = "(robot:robot, ball:ball, cup:cup, sticky-table-0:table)"
+    save_path = f"{save_path}_{suffix}"
+    classifier_save_path = f"{save_path}.sampler_classifier"
+    if os.path.exists(classifier_save_path):
+        with open(classifier_save_path, "rb") as f:
+            classifier = pkl.load(f)
+        print(f"Loaded sampler classifier from {classifier_save_path}.")
+    data_save_path = f"{save_path}.sampler_classifier_data"
+    if os.path.exists(data_save_path):
+        with open(data_save_path, "rb") as f:
+            data = pkl.load(f)
+            print(f"Loaded classifier training data from {data_save_path}.")
+            candidates = list(data[0])
+    else:
+        candidates = []
+
+    cmap = colormaps.get_cmap('RdYlGn')
+    norm = Normalize(vmin=0.0, vmax=1.0)
+
+    # Classify the candidates.
+    predictions = []
+    for x in candidates:
+        prediction = classifier.predict_proba(x)
+        predictions.append(prediction)
+
+    # Visualize the classifications.
+    fig, axes = plt.subplots(1, 2)
+    plt.suptitle(f"Cycle {online_learning_cycle}")
+
+    radius = 5e-3
+    for i, ax in enumerate(axes.flat):
+        ax.set_xlabel("x")
+        if i == 0:
+            ax.set_ylabel("y")
+            ax.set_title("Ground Truth")
+        else:
+            ax.set_title("Predictions")
+        ax.set_xlim((-0.15, 0.15))
+        ax.set_ylim((-0.15, 0.15))
+
+        for candidate, prediction in zip(candidates, predictions):
+
+            _, table_radius, sticky, sticky_region_x, sticky_region_y, \
+                sticky_region_radius, table_x, table_y, param_x, param_y = candidate
+
+            assert table_radius > 0
+            assert sticky
+
+            # Get (x, y) in table frame.
+            act_x_table = param_x - table_x
+            act_y_table = param_y - table_y
+
+            # Get (x, y) in sticky region frame.
+            act_x_sticky = act_x_table - sticky_region_x
+            act_y_sticky = act_y_table - sticky_region_y
+
+            # Check if less than radius away.
+            in_sticky_region = (act_x_sticky**2 + act_y_sticky**2 <
+                                sticky_region_radius**2)
+
+            if i == 0:
+                color = cmap(norm(in_sticky_region))
+            else:
+                color = cmap(norm(prediction))
+
+            circle = plt.Circle((act_x_sticky, act_y_sticky),
+                                radius,
+                                color=color,
+                                alpha=1.0)
+            ax.add_patch(circle)
+
+    return utils.fig2data(fig, dpi=150)
+
+
+def _create_example_state(env: BaseEnv) -> State:
+    init_obs = env.reset("train", 0)
+    assert isinstance(init_obs, State)
+    return init_obs.copy()
+
+
+if __name__ == "__main__":
+    _main()