diff --git a/scripts/experiments/icra/mug/mug.pynb b/scripts/experiments/icra/mug/mug.ipynb
similarity index 82%
rename from scripts/experiments/icra/mug/mug.pynb
rename to scripts/experiments/icra/mug/mug.ipynb
index a585d41a..afc8a2cc 100644
--- a/scripts/experiments/icra/mug/mug.pynb
+++ b/scripts/experiments/icra/mug/mug.ipynb
@@ -29,7 +29,7 @@
      "output_type": "stream",
      "text": [
       "You can open the visualizer by visiting the following URL:\n",
-      "http://127.0.0.1:7014/static/\n"
+      "http://127.0.0.1:7016/static/\n"
      ]
     }
    ],
@@ -40,58 +40,6 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d705f5f3-499d-4f43-b689-41ba648a2a1f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_dir = os.path.join(b.utils.get_assets_dir(),\"ycb_video_models/models/025_mug\")\n",
-    "mesh = b.utils.mesh.load_mesh(os.path.join(model_dir, \"textured_simple.obj\"))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7405b61a-4464-4ac3-ae86-f9eecc0965eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b.clear()\n",
-    "num_colors = 2\n",
-    "colors = b.viz.distinct_colors(num_colors)\n",
-    "offset = b.transform_from_pos(jnp.array([0.003, 0.0,0.0]))\n",
-    "for i in range(num_colors):\n",
-    "    b.show_trimesh(f\"{i}\", mesh, color=colors[i])\n",
-    "    b.set_pose(f\"{i}\", offset @ b.transform_from_axis_angle(jnp.array([0.0, 0.0, 1.0]), i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5eab559-3fd7-4964-a903-9f1a50ad3842",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(Array([0.116966, 0.093075, 0.081384], dtype=float32),\n",
-       " Array([[1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 8.2690008e-03],\n",
-       "        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.1250106e-04],\n",
-       "        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.6890001e-03],\n",
-       "        [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0000000e+00]],      dtype=float32))"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.utils.aabb(mesh.vertices)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
    "id": "a42856ac-4db2-43cd-a4e7-6b93bc550f12",
    "metadata": {},
    "outputs": [
@@ -140,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "f4648f31-caf1-4792-bd83-9d652a8c5e4b",
    "metadata": {},
    "outputs": [],
@@ -156,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "3277a542-3698-40f7-a998-d7febb9591eb",
    "metadata": {},
    "outputs": [],
@@ -251,145 +199,845 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "4a64e88e-4df4-4ae4-bd21-24c04fdc7782",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['002_master_chef_can',\n",
-       " '003_cracker_box',\n",
-       " '004_sugar_box',\n",
-       " '005_tomato_soup_can',\n",
-       " '006_mustard_bottle',\n",
-       " '007_tuna_fish_can',\n",
-       " '008_pudding_box',\n",
-       " '009_gelatin_box',\n",
-       " '010_potted_meat_can',\n",
-       " '011_banana',\n",
-       " '019_pitcher_base',\n",
-       " '021_bleach_cleanser',\n",
-       " '024_bowl',\n",
-       " '025_mug',\n",
-       " '035_power_drill',\n",
-       " '036_wood_block',\n",
-       " '037_scissors',\n",
-       " '040_large_marker',\n",
-       " '051_large_clamp',\n",
-       " '052_extra_large_clamp',\n",
-       " '061_foam_brick']"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.utils.ycb_loader.MODEL_NAMES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "772bb4b0-af42-4842-9af6-94752b30a707",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_depth_image(image):\n",
-    "    mval = image[image < image.max()].max()\n",
-    "    return b.get_depth_image(image, max=mval)\n",
-    "\n",
-    "def get_poses_non_jit(contact_params, id_table, id):\n",
-    "    sg = b.scene_graph.SceneGraph(\n",
-    "        root_poses=jnp.array([table_pose, jnp.eye(4)]),\n",
-    "        box_dimensions=jnp.array([b.RENDERER.model_box_dims[id_table], b.RENDERER.model_box_dims[id]]),\n",
-    "        parents=jnp.array([-1, 0]),\n",
-    "        contact_params=jnp.array([jnp.zeros(3), contact_params]),\n",
-    "        face_parent=jnp.array([-1,2]),\n",
-    "        face_child=jnp.array([-1,3]),\n",
-    "    )\n",
-    "    poses = sg.get_poses()\n",
-    "    return poses\n",
-    "get_poses = jax.jit(get_poses_non_jit)\n",
-    "\n",
-    "def render_image_non_jit(contact_params):\n",
-    "    id_table = 21\n",
-    "    id = 13\n",
-    "    poses = get_poses_non_jit(contact_params, id_table, id)\n",
-    "    img = b.RENDERER.render(\n",
-    "        poses , jnp.array([id_table, id])\n",
-    "    )[...,:3]\n",
-    "    return img\n",
-    "render_image = jax.jit(render_image_non_jit)\n",
-    "\n",
-    "scorer = lambda obs, c, var, outlier_prob, outlier_volume: threedp3_likelihood(\n",
-    "    obs, render_image_non_jit(c), var, outlier_prob, outlier_volume, 4\n",
-    ")\n",
-    "scorer_jit = jax.jit(scorer)\n",
-    "sweep_scorer = jax.jit(jax.vmap(\n",
-    "    scorer \n",
-    "    ,in_axes=(None, 0, None, None, None)))\n",
-    "\n",
-    "# sweep_scorer = jax.jit(jax.vmap(jax.vmap(jax.vmap(\n",
-    "#     scorer, \n",
-    "#     ,in_axes=(None, None, None, 0, None))\n",
-    "#     ,in_axes=(None, None, 0, None, None))\n",
-    "#     ,in_axes=(None, 0, None, None, None)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "fb05d62f-8b7f-4ed2-873c-530302c10964",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "width = 0.01\n",
-    "ang = jnp.pi\n",
-    "contact_param_deltas = b.utils.make_translation_grid_enumeration_3d(\n",
-    "    -width, -width, -ang,\n",
-    "    width, width, ang,\n",
-    "    21,21,300\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "34bdcc5b-c781-4f35-bc8a-e89d35033465",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = jax.random.PRNGKey(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "02d80355-07ff-49af-8233-71d276f95303",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "variance = 0.0001\n",
-    "outlier_prob = 0.0001\n",
-    "outlier_volume = 1.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "cae7a753-e4f7-4cc3-beb5-5d149e37119c",
-   "metadata": {},
-   "outputs": [
+     "data": {
+      "text/plain": [
+       "['002_master_chef_can',\n",
+       " '003_cracker_box',\n",
+       " '004_sugar_box',\n",
+       " '005_tomato_soup_can',\n",
+       " '006_mustard_bottle',\n",
+       " '007_tuna_fish_can',\n",
+       " '008_pudding_box',\n",
+       " '009_gelatin_box',\n",
+       " '010_potted_meat_can',\n",
+       " '011_banana',\n",
+       " '019_pitcher_base',\n",
+       " '021_bleach_cleanser',\n",
+       " '024_bowl',\n",
+       " '025_mug',\n",
+       " '035_power_drill',\n",
+       " '036_wood_block',\n",
+       " '037_scissors',\n",
+       " '040_large_marker',\n",
+       " '051_large_clamp',\n",
+       " '052_extra_large_clamp',\n",
+       " '061_foam_brick']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.utils.ycb_loader.MODEL_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "772bb4b0-af42-4842-9af6-94752b30a707",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_depth_image(image):\n",
+    "    mval = image[image < image.max()].max()\n",
+    "    return b.get_depth_image(image, max=mval)\n",
+    "\n",
+    "def get_poses_non_jit(contact_params, id_table, id):\n",
+    "    sg = b.scene_graph.SceneGraph(\n",
+    "        root_poses=jnp.array([table_pose, jnp.eye(4)]),\n",
+    "        box_dimensions=jnp.array([b.RENDERER.model_box_dims[id_table], b.RENDERER.model_box_dims[id]]),\n",
+    "        parents=jnp.array([-1, 0]),\n",
+    "        contact_params=jnp.array([jnp.zeros(3), contact_params]),\n",
+    "        face_parent=jnp.array([-1,2]),\n",
+    "        face_child=jnp.array([-1,3]),\n",
+    "    )\n",
+    "    poses = sg.get_poses()\n",
+    "    return poses\n",
+    "get_poses = jax.jit(get_poses_non_jit)\n",
+    "\n",
+    "def render_image_non_jit(contact_params):\n",
+    "    id_table = 21\n",
+    "    id = 13\n",
+    "    poses = get_poses_non_jit(contact_params, id_table, id)\n",
+    "    img = b.RENDERER.render(\n",
+    "        poses , jnp.array([id_table, id])\n",
+    "    )[...,:3]\n",
+    "    return img\n",
+    "render_image = jax.jit(render_image_non_jit)\n",
+    "\n",
+    "scorer = lambda obs, c, var, outlier_prob, outlier_volume: threedp3_likelihood(\n",
+    "    obs, render_image_non_jit(c), var, outlier_prob, outlier_volume, 3\n",
+    ")\n",
+    "scorer_jit = jax.jit(scorer)\n",
+    "sweep_scorer = jax.jit(jax.vmap(\n",
+    "    scorer \n",
+    "    ,in_axes=(None, 0, None, None, None)))\n",
+    "\n",
+    "# sweep_scorer = jax.jit(jax.vmap(jax.vmap(jax.vmap(\n",
+    "#     scorer, \n",
+    "#     ,in_axes=(None, None, None, 0, None))\n",
+    "#     ,in_axes=(None, None, 0, None, None))\n",
+    "#     ,in_axes=(None, 0, None, None, None)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fb05d62f-8b7f-4ed2-873c-530302c10964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "width = 0.015\n",
+    "ang = jnp.pi\n",
+    "contact_param_deltas = b.utils.make_translation_grid_enumeration_3d(\n",
+    "    -width, -width, -ang,\n",
+    "    width, width, ang,\n",
+    "    17,17,100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "34bdcc5b-c781-4f35-bc8a-e89d35033465",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = jax.random.PRNGKey(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "02d80355-07ff-49af-8233-71d276f95303",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variance = 0.0001\n",
+    "outlier_prob = 0.001\n",
+    "outlier_volume = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cae7a753-e4f7-4cc3-beb5-5d149e37119c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                            | 0/50 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37091.543\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  2%|█                                                   | 1/50 [00:10<08:43, 10.68s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37113.234\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  4%|██                                                  | 2/50 [00:11<04:00,  5.01s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37140.016\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  6%|███                                                 | 3/50 [00:12<02:30,  3.20s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37182.41\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  8%|████▏                                               | 4/50 [00:13<01:48,  2.35s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37120.953\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 10%|█████▏                                              | 5/50 [00:14<01:24,  1.87s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36992.89\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 12%|██████▏                                             | 6/50 [00:15<01:10,  1.59s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37059.883\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 14%|███████▎                                            | 7/50 [00:16<01:00,  1.41s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37271.01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 16%|████████▎                                           | 8/50 [00:17<00:53,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37184.707\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 18%|█████████▎                                          | 9/50 [00:18<00:49,  1.20s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37206.496\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██████████▏                                        | 10/50 [00:19<00:46,  1.15s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37379.594\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 22%|███████████▏                                       | 11/50 [00:21<00:43,  1.11s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37116.023\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 24%|████████████▏                                      | 12/50 [00:22<00:41,  1.09s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37066.914\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 26%|█████████████▎                                     | 13/50 [00:23<00:39,  1.08s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37056.92\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 28%|██████████████▎                                    | 14/50 [00:24<00:38,  1.06s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37113.008\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 30%|███████████████▎                                   | 15/50 [00:25<00:36,  1.05s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37096.46\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 32%|████████████████▎                                  | 16/50 [00:26<00:35,  1.04s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37369.363\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 34%|█████████████████▎                                 | 17/50 [00:27<00:34,  1.04s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37124.203\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 36%|██████████████████▎                                | 18/50 [00:28<00:33,  1.04s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36950.24\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 38%|███████████████████▍                               | 19/50 [00:29<00:32,  1.04s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37104.336\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 40%|████████████████████▍                              | 20/50 [00:30<00:31,  1.04s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37115.848\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_813201/2689616434.py:26: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
+      "  fig = plt.figure(constrained_layout=True)\n",
+      " 42%|█████████████████████▍                             | 21/50 [00:31<00:30,  1.04s/it]"
+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
+      "37206.098\n"
+     ]
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+      "37271.5\n"
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+      "37400.11\n"
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+      "37134.28\n"
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+      "37064.5\n"
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+      "37063.695\n"
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+      "36942.914\n"
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+      "37123.758\n"
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+    {
+     "name": "stderr",
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+      "37085.445\n"
+     ]
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+      "37291.316\n"
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+    {
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+      "37064.703\n"
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+    {
+     "name": "stderr",
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+      "36939.305\n"
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+    {
+     "name": "stderr",
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+      "37308.234\n"
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     {
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-      " 40%|███████████████▌                       | 20/50 [01:24<02:08,  4.29s/it]/var/tmp/ipykernel_624622/488378740.py:26: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
-      "  fig = plt.figure(constrained_layout=True)\n",
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+    {
+     "name": "stderr",
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+    {
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+    {
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+      "37269.11\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+      "37156.082\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37156.617\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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      ]
     },
     {
@@ -855,7 +1503,7 @@
     "    # gt_contact = jnp.array([-0.03, -0.09, -0.51 + jnp.pi ])\n",
     "    observation = render_image(gt_contact)\n",
     "    get_depth_image(observation[...,2])\n",
-    "    \n",
+    "    print(scorer_jit(observation, gt_contact, variance, outlier_prob, outlier_volume))\n",
     "    contact_param_grid = gt_contact + contact_param_deltas\n",
     "    \n",
     "    weights =  jnp.concatenate([\n",
@@ -901,13 +1549,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "id": "49c9bee1-c81d-43a9-b720-244b9c16c892",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -974,53 +1622,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": null,
    "id": "e397b20d-889c-458a-be01-79cf19b509de",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['002_master_chef_can',\n",
-       " '003_cracker_box',\n",
-       " '004_sugar_box',\n",
-       " '005_tomato_soup_can',\n",
-       " '006_mustard_bottle',\n",
-       " '007_tuna_fish_can',\n",
-       " '008_pudding_box',\n",
-       " '009_gelatin_box',\n",
-       " '010_potted_meat_can',\n",
-       " '011_banana',\n",
-       " '019_pitcher_base',\n",
-       " '021_bleach_cleanser',\n",
-       " '024_bowl',\n",
-       " '025_mug',\n",
-       " '035_power_drill',\n",
-       " '036_wood_block',\n",
-       " '037_scissors',\n",
-       " '040_large_marker',\n",
-       " '051_large_clamp',\n",
-       " '052_extra_large_clamp',\n",
-       " '061_foam_brick']"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "b.utils.ycb_loader.MODEL_NAMES"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e17f43d-833a-4984-ae79-9a1b262548f3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,