diff --git a/examples/teleop/inspect_parquet_duckdb.ipynb b/examples/teleop/inspect_parquet_duckdb.ipynb
new file mode 100644
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+++ b/examples/teleop/inspect_parquet_duckdb.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85351314",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(┌─────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬─────────┬─────────┬─────────┬─────────┐\n",
+       " │ column_name │                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 column_type                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  │  null   │   key   │ default │  extra  │\n",
+       " │   varchar   │                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   varchar                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    │ varchar │ varchar │ varchar │ varchar │\n",
+       " ├─────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┼─────────┼─────────┼─────────┼─────────┤\n",
+       " │ obs         │ STRUCT(\"left\" STRUCT(tquat DOUBLE[], joints DOUBLE[], xyzrpy DOUBLE[], gripper DOUBLE[]), \"right\" STRUCT(tquat DOUBLE[], joints DOUBLE[], xyzrpy DOUBLE[], gripper DOUBLE[]), frames STRUCT(head STRUCT(rgb STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]), depth STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[])), left_wrist STRUCT(rgb STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]), depth STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[])), right_wrist STRUCT(rgb STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]), depth STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[])))) │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ info        │ STRUCT(\"left\" STRUCT(collision BOOLEAN, ik_success BOOLEAN, is_sim_converged BOOLEAN, gripper_width DOUBLE, is_grasped BOOLEAN, terminated BOOLEAN, truncated BOOLEAN), \"right\" STRUCT(collision BOOLEAN, ik_success BOOLEAN, is_sim_converged BOOLEAN, gripper_width DOUBLE, is_grasped BOOLEAN, terminated BOOLEAN, truncated BOOLEAN), camera_available BOOLEAN, frame_timestamp DOUBLE, is_grasped BOOLEAN, success BOOLEAN)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ reward      │ DOUBLE                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ step        │ BIGINT                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ uuid        │ VARCHAR                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ success     │ BOOLEAN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ action      │ STRUCT(\"left\" STRUCT(tquat DOUBLE[], gripper BIGINT[]), \"right\" STRUCT(tquat DOUBLE[], gripper BIGINT[]))                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ instruction │ VARCHAR                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ timestamp   │ DOUBLE                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ head        │ STRUCT(rgb STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]), depth STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]))                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ left_wrist  │ STRUCT(rgb STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]), depth STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]))                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " │ right_wrist │ STRUCT(rgb STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]), depth STRUCT(\"data\" BLOB, intrinsics DOUBLE[], extrinsics DOUBLE[], intrinsics_shape BIGINT[], extrinsics_shape BIGINT[]))                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   │ YES     │ NULL    │ NULL    │ NULL    │\n",
+       " └─────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴─────────┴─────────┴─────────┴─────────┘\n",
+       "   12 rows                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  6 columns,\n",
+       " ┌──────────┐\n",
+       " │ n_frames │\n",
+       " │  int64   │\n",
+       " ├──────────┤\n",
+       " │     5321 │\n",
+       " └──────────┘)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import duckdb\n",
+    "path = \"/home/tobi/coding/rcs_clones/robot-control-stack/data_grasp\"\n",
+    "duckdb.sql(f\"describe select *, unnest(obs.frames) from read_parquet('{path}')\"), duckdb.sql(f\"select count(*) as n_frames from read_parquet('{path}')\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8085186f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
+       "│                                                                                                                                                                                                                          info                                                                                                                                                                                                                          │\n",
+       "│            struct(\"left\" struct(collision boolean, ik_success boolean, is_sim_converged boolean, gripper_width double, is_grasped boolean, terminated boolean, truncated boolean), \"right\" struct(collision boolean, ik_success boolean, is_sim_converged boolean, gripper_width double, is_grasped boolean, terminated boolean, truncated boolean), camera_available boolean, frame_timestamp double, is_grasped boolean, success boolean)            │\n",
+       "├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
+       "│ {'left': {'collision': false, 'ik_success': true, 'is_sim_converged': true, 'gripper_width': 1.0, 'is_grasped': false, 'terminated': false, 'truncated': false}, 'right': {'collision': false, 'ik_success': true, 'is_sim_converged': true, 'gripper_width': 0.4277359972585904, 'is_grasped': true, 'terminated': false, 'truncated': false}, 'camera_available': true, 'frame_timestamp': 5.5799999999996075, 'is_grasped': false, 'success': true} │\n",
+       "└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"SELECT \n",
+    "    info\n",
+    "FROM read_parquet('{path}')\n",
+    "WHERE success\n",
+    "limit 1\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9bf6a837",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(┌────────────┐\n",
+       " │ successful │\n",
+       " │   int64    │\n",
+       " ├────────────┤\n",
+       " │         27 │\n",
+       " └────────────┘,\n",
+       " ┌────────────┐\n",
+       " │ n_episodes │\n",
+       " │   int64    │\n",
+       " ├────────────┤\n",
+       " │         30 │\n",
+       " └────────────┘)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "    \"SELECT count(DISTINCT uuid) as successful \"\n",
+    "    f\"FROM read_parquet('{path}') \"\n",
+    "    \"WHERE success\"\n",
+    "),duckdb.sql(\n",
+    "    \"SELECT count(DISTINCT uuid) as n_episodes \"\n",
+    "    f\"FROM read_parquet('{path}') \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f1ee638e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌────────────────────┬────────────────────┬───────────────────┐\n",
+       "│  (max(step) / 30)  │  (min(step) / 30)  │ (avg(step) / 30)  │\n",
+       "│       double       │       double       │      double       │\n",
+       "├────────────────────┼────────────────────┼───────────────────┤\n",
+       "│ 17.533333333333335 │ 1.0666666666666667 │ 5.346779661016949 │\n",
+       "└────────────────────┴────────────────────┴───────────────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"SELECT \n",
+    "    MAX(step)/30, MIN(step)/30, AVG(step)/30\n",
+    "FROM read_parquet('{path}')\n",
+    "WHERE success\n",
+    "    \"\"\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97808e66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "episode uuid: bda71d1e8bf44a6e9a0467ca9e1e7bfd, success: False, n_steps: 150, info: {'left': {'collision': False, 'ik_success': True, 'is_sim_converged': True, 'gripper_width': 1.0, 'is_grasped': False, 'terminated': False, 'truncated': False}, 'right': {'collision': False, 'ik_success': True, 'is_sim_converged': True, 'gripper_width': 1.0, 'is_grasped': False, 'terminated': False, 'truncated': False}, 'camera_available': True, 'frame_timestamp': 4.899999999999682, 'is_grasped': False, 'success': False}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import io\n",
+    "from matplotlib import pyplot as plt\n",
+    "from PIL import Image\n",
+    "import duckdb\n",
+    "episode = 0\n",
+    "step = 100\n",
+    "\n",
+    "\n",
+    "uuids = duckdb.sql(f\"SELECT DISTINCT uuid FROM read_parquet('{path}') WHERE success\").fetchnumpy()\n",
+    "# uuids\n",
+    "uuid1 = uuids[\"uuid\"][episode]\n",
+    "rel = duckdb.read_parquet(path)\n",
+    "success = rel.filter(f\"uuid='{uuid1}' and step={step}\").select(\"success\").fetchone()[0]\n",
+    "info = rel.filter(f\"uuid='{uuid1}' and step={step}\").select(\"info\").fetchone()[0]\n",
+    "count = rel.filter(f\"uuid='{uuid1}'\").count(\"*\").fetchone()[0]\n",
+    "print(f\"episode uuid: {uuid1}, success: {success}, n_steps: {count}, info: {info}\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a figure with 1 row and 2 columns\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(20, 5))\n",
+    "\n",
+    "\n",
+    "img1_data = rel.filter(f\"uuid='{uuid1}' and step={step}\").select(\"obs.frames.head.rgb.data\").fetchone()[0]\n",
+    "image1 = Image.open(io.BytesIO(img1_data))\n",
+    "ax[0].imshow(image1)\n",
+    "ax[0].set_title(\"Head Camera\")\n",
+    "\n",
+    "img2_data = rel.filter(f\"uuid='{uuid1}' and step={step}\").select(\"obs.frames.left_wrist.rgb.data\").fetchone()[0]\n",
+    "image2 = Image.open(io.BytesIO(img2_data))\n",
+    "ax[1].imshow(image2)\n",
+    "ax[1].set_title(\"Left Wrist Camera\") # Optional title\n",
+    "\n",
+    "img3_data = rel.filter(f\"uuid='{uuid1}' and step={step}\").select(\"obs.frames.right_wrist.rgb.data\").fetchone()[0]\n",
+    "image3 = Image.open(io.BytesIO(img3_data))\n",
+    "ax[2].imshow(image3)\n",
+    "ax[2].set_title(\"Right Wrist Camera\") # Optional title\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "758f3147",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌───────────────────┐\n",
+       "│     timestamp     │\n",
+       "│      double       │\n",
+       "├───────────────────┤\n",
+       "│ 1777204228.532817 │\n",
+       "│ 1777204228.583433 │\n",
+       "│ 1777204228.635595 │\n",
+       "│ 1777204228.686985 │\n",
+       "│ 1777204228.735731 │\n",
+       "│   1777204228.7872 │\n",
+       "│  1777204228.83733 │\n",
+       "│ 1777204228.884319 │\n",
+       "│ 1777204228.934861 │\n",
+       "│ 1777204076.763415 │\n",
+       "│         ·         │\n",
+       "│         ·         │\n",
+       "│         ·         │\n",
+       "│ 1777204207.432611 │\n",
+       "│ 1777204207.474623 │\n",
+       "│  1777204207.51622 │\n",
+       "│ 1777204207.553477 │\n",
+       "│ 1777204207.595021 │\n",
+       "│ 1777204207.629543 │\n",
+       "│ 1777204207.667513 │\n",
+       "│ 1777204207.704982 │\n",
+       "│ 1777204207.745933 │\n",
+       "│ 1777204207.783519 │\n",
+       "└───────────────────┘\n",
+       "      5321 rows    \n",
+       "     (20 shown)     "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"SELECT \n",
+    "    timestamp\n",
+    "FROM read_parquet('{path}')\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "14514ec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌──────────────────────┐\n",
+       "│ average_frequency_hz │\n",
+       "│        double        │\n",
+       "├──────────────────────┤\n",
+       "│    20.90057259306587 │\n",
+       "└──────────────────────┘"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"WITH episode_stats AS (\n",
+    "    SELECT\n",
+    "        uuid,\n",
+    "        COUNT(*) AS frame_count,\n",
+    "        MAX(\"timestamp\") - MIN(\"timestamp\") AS duration_seconds\n",
+    "    FROM read_parquet('{path}')\n",
+    "    GROUP BY uuid\n",
+    "    HAVING duration_seconds > 0 -- Prevents division by zero for single-frame episodes\n",
+    ")\n",
+    "SELECT \n",
+    "    AVG(frame_count / duration_seconds) AS average_frequency_hz\n",
+    "FROM episode_stats;\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b67b4bf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌────────────────────┐\n",
+       "│  std_frequency_hz  │\n",
+       "│       double       │\n",
+       "├────────────────────┤\n",
+       "│ 1.9147779001859897 │\n",
+       "└────────────────────┘"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"WITH episode_stats AS (\n",
+    "    SELECT\n",
+    "        uuid,\n",
+    "        COUNT(*) AS frame_count,\n",
+    "        MAX(\"timestamp\") - MIN(\"timestamp\") AS duration_seconds\n",
+    "    FROM read_parquet('{path}')\n",
+    "    GROUP BY uuid\n",
+    "    HAVING duration_seconds > 0 -- Prevents division by zero for single-frame episodes\n",
+    ")\n",
+    "SELECT \n",
+    "    STDDEV(frame_count / duration_seconds) AS std_frequency_hz\n",
+    "FROM episode_stats;\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0eac2e74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌───────────────────┬───────────────────┐\n",
+       "│ mean_frequency_hz │ std_frequency_hz  │\n",
+       "│      double       │      double       │\n",
+       "├───────────────────┼───────────────────┤\n",
+       "│ 21.94798549029336 │ 4.965794976134775 │\n",
+       "└───────────────────┴───────────────────┘"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"WITH frame_intervals AS (\n",
+    "    SELECT \n",
+    "        uuid,\n",
+    "        \"timestamp\",\n",
+    "        -- Get the timestamp of the *next* frame within the same episode\n",
+    "        LEAD(\"timestamp\") OVER (PARTITION BY uuid ORDER BY \"timestamp\") AS next_timestamp\n",
+    "    FROM read_parquet('{path}')\n",
+    "),\n",
+    "instantaneous_frequencies AS (\n",
+    "    SELECT\n",
+    "        -- Frequency = 1 / time_difference\n",
+    "        1.0 / (next_timestamp - \"timestamp\") AS freq_hz\n",
+    "    FROM frame_intervals\n",
+    "    WHERE next_timestamp IS NOT NULL \n",
+    "      AND (next_timestamp - \"timestamp\") > 1e-6 -- Filter out zero/tiny diffs to prevent errors\n",
+    ")\n",
+    "SELECT \n",
+    "    AVG(freq_hz) AS mean_frequency_hz,\n",
+    "    STDDEV(freq_hz) AS std_frequency_hz\n",
+    "FROM instantaneous_frequencies;\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a7b09572",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "┌──────────────────────────────────┬────────────────────┬─────────────┬────────────────────┬─────────────┐\n",
+       "│               uuid               │    min_freq_hz     │ step_at_min │    max_freq_hz     │ step_at_max │\n",
+       "│             varchar              │       double       │    int64    │       double       │    int64    │\n",
+       "├──────────────────────────────────┼────────────────────┼─────────────┼────────────────────┼─────────────┤\n",
+       "│ bda71d1e8bf44a6e9a0467ca9e1e7bfd │ 5.9396196596507576 │          31 │ 28.946996466431095 │          77 │\n",
+       "│ 66cbb1ca310f435e99f780de83895bd9 │  5.826619916316131 │          63 │ 28.967788275595336 │          89 │\n",
+       "│ 1c6853006d9e4b02b2a31dac40fcd2bd │ 6.0373760477197385 │          95 │ 30.792018441569883 │           2 │\n",
+       "│ 3aa324d79982427182cc5df4f465d1bb │ 4.8488334857395206 │          31 │ 29.719435981010417 │         108 │\n",
+       "│ 4497782de6c34767b0aaa425b2e26eb3 │ 6.3683313114258775 │          95 │  33.85697795500593 │          21 │\n",
+       "│ a7757dcc23904a8b82c1274c42d9715c │  5.752982583195486 │          31 │ 29.731865514528145 │           6 │\n",
+       "│ 14da252f8ffe4293bcd2ade05d7f0519 │  4.957952658175478 │         447 │ 24.100486111909166 │         241 │\n",
+       "│ 27c6b0e231094c76bbe8e3e93460cd2f │  6.442051846004037 │          31 │ 21.560997676474823 │          17 │\n",
+       "│ 6d3bac5e8e7641419677573d1c09d817 │ 6.4079591044785245 │         159 │ 30.754087782845243 │          48 │\n",
+       "│ c8a8d90fb7484ebf8852760ce2046007 │  6.316199712373222 │         127 │ 30.063462710102858 │          91 │\n",
+       "│                ·                 │          ·         │           · │          ·         │           · │\n",
+       "│                ·                 │          ·         │           · │          ·         │           · │\n",
+       "│                ·                 │          ·         │           · │          ·         │           · │\n",
+       "│ 73ce9d02be9c4d9fb8a3ab1356062aca │  6.078736688328628 │          31 │ 28.515221972941735 │         119 │\n",
+       "│ a70da27eb26046d58cb03b18e82947db │  5.946263510678114 │          31 │ 30.023650680028634 │          88 │\n",
+       "│ 08e6a2023e794cdb93e27709cd73bcee │  6.197898718839124 │          31 │  31.30965497678446 │           5 │\n",
+       "│ 36cfab4f02cf48b88dfd58b5c0c71596 │  6.028672207162015 │          31 │ 29.214144917845527 │          74 │\n",
+       "│ 0686f634cc844c09be7dce44e519346c │  6.107388472520928 │         159 │ 29.564001353332582 │         148 │\n",
+       "│ 11b8aef48c4641fe8b2fa12f6aa269e8 │  6.009797810895659 │          31 │ 29.010866182034487 │         139 │\n",
+       "│ 730e310a0095461794f2eedb0faf15a0 │ 6.5395399890235995 │          31 │ 30.061523465496975 │          15 │\n",
+       "│ 8174017865424b349f766454a8e63a6d │  6.308438315946528 │          31 │  30.07337831346034 │          52 │\n",
+       "│ 84399ec490944ff4ac15b13ff9c1c3e8 │ 6.1951340557019465 │          31 │ 30.397912740976953 │          14 │\n",
+       "│ af89603bb83742828785b22507b4406d │   3.75373892373768 │          31 │  28.77699105329601 │          94 │\n",
+       "└──────────────────────────────────┴────────────────────┴─────────────┴────────────────────┴─────────────┘\n",
+       "  30 rows (20 shown)                                                                           5 columns"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duckdb.sql(\n",
+    "f\"\"\"WITH frame_intervals AS (\n",
+    "    SELECT \n",
+    "        uuid,\n",
+    "        step,\n",
+    "        \"timestamp\",\n",
+    "        LEAD(\"timestamp\") OVER (PARTITION BY uuid ORDER BY step) AS next_timestamp\n",
+    "    FROM read_parquet('{path}')\n",
+    "),\n",
+    "instantaneous_frequencies AS (\n",
+    "    SELECT\n",
+    "        uuid,\n",
+    "        step,\n",
+    "        1.0 / (next_timestamp - \"timestamp\") AS freq_hz\n",
+    "    FROM frame_intervals\n",
+    "    WHERE next_timestamp IS NOT NULL \n",
+    "      AND (next_timestamp - \"timestamp\") > 1e-6\n",
+    ")\n",
+    "SELECT \n",
+    "    uuid,\n",
+    "    -- Get the Min Frequency and the step it happened at\n",
+    "    MIN(freq_hz) AS min_freq_hz,\n",
+    "    ARG_MIN(step, freq_hz) AS step_at_min,\n",
+    "    \n",
+    "    -- Get the Max Frequency and the step it happened at\n",
+    "    MAX(freq_hz) AS max_freq_hz,\n",
+    "    ARG_MAX(step, freq_hz) AS step_at_max\n",
+    "FROM instantaneous_frequencies\n",
+    "GROUP BY uuid;\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rcs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/teleop/requirements.txt b/examples/teleop/requirements.txt
index 51b07444..3707e128 100644
--- a/examples/teleop/requirements.txt
+++ b/examples/teleop/requirements.txt
@@ -1,4 +1,6 @@
 # simpub @ git+https://github.com/intuitive-robots/SimPublisher.git@a0ebdbfba86f58aaa6360eb3b9da7b74a9c9b1dd # doesnt work
 dynamixel_sdk
 pynput
-scipy
\ No newline at end of file
+scipy
+jupyter
+mutplotlib
\ No newline at end of file
diff --git a/python/rcs/__init__.py b/python/rcs/__init__.py
index 390df41b..ba98dcd1 100644
--- a/python/rcs/__init__.py
+++ b/python/rcs/__init__.py
@@ -125,6 +125,11 @@ class RobotMetaConfig:
     common.GripperType("Robotiq2F85"): "assets/grippers/robotiq_2f85/robotiq_2f85.xml",
 }
 
+GRIPPER_OFFSETS: dict[common.GripperType, common.Pose] = {
+    common.GripperType.FrankaHand: common.Pose(pose_matrix=common.FrankaHandTCPOffset()),
+    common.GripperType("Robotiq2F85"): common.Pose(translation=np.array([0.0, 0.0, 0.1628])),
+}
+
 SCENE_PATHS: dict[str, str] = {"empty_world": "assets/scenes/empty_world/scene.xml"}
 
 OBJECT_PATHS: dict[str, str] = {
diff --git a/python/rcs/__main__.py b/python/rcs/__main__.py
index 743c68a0..f82ce406 100644
--- a/python/rcs/__main__.py
+++ b/python/rcs/__main__.py
@@ -3,6 +3,21 @@
 
 import typer
 from rcs.envs.storage_wrapper import StorageWrapper
+from rcs.lerobot_joint_converter import (
+    DEFAULT_CAMERAS,
+    DEFAULT_DATASET_PATHS,
+    DEFAULT_FPS,
+    DEFAULT_GRIPPER_TYPE,
+    DEFAULT_HF_DATA_DIR,
+    DEFAULT_IMAGE_BATCH_SIZE,
+    DEFAULT_JOINTS,
+    DEFAULT_PER_ROBOT_ARM_DIM,
+    DEFAULT_REPO_ID,
+    DEFAULT_ROBOT_KEYS,
+    DEFAULT_ROBOT_TYPE,
+    camera_specs_to_configs,
+    run_conversion,
+)
 from rcs.sim.replayer import replay as replay_dataset
 
 app = typer.Typer()
@@ -67,5 +82,80 @@ def replay(
     )
 
 
+@app.command("lerobot-convert")
+def lerobot_convert(
+    output: Annotated[
+        Path,
+        typer.Argument(
+            help="Output directory for the LeRobot dataset. Example: --output ./data_lerobot",
+        ),
+    ] = Path(DEFAULT_HF_DATA_DIR),
+    dataset_paths: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--dataset-path",
+            help="Input parquet path or glob. Repeat for multiple datasets. Example: --dataset-path /data/session1 --dataset-path /data/session2",
+        ),
+    ] = None,
+    repo_id: Annotated[
+        str, typer.Option(help="LeRobot repo id metadata. Example: --repo-id myorg/grasp_v2")
+    ] = DEFAULT_REPO_ID,
+    robot_type: Annotated[
+        str, typer.Option(help="Robot type for metadata and IK model lookup. Example: --robot-type fr3")
+    ] = DEFAULT_ROBOT_TYPE,
+    fps: Annotated[int, typer.Option(help="Dataset frames per second. Example: --fps 30")] = DEFAULT_FPS,
+    robot_keys: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--robot-key",
+            help="Robot keys to concatenate. Repeat for multiple robots. Example: --robot-key left --robot-key right",
+        ),
+    ] = None,
+    joints: Annotated[
+        bool, typer.Option(help="Whether absolute_action is already in joint space. Example: --joints")
+    ] = DEFAULT_JOINTS,
+    gripper_type: Annotated[
+        str, typer.Option(help="Gripper type used to derive TCP offset. Example: --gripper-type Robotiq2F85")
+    ] = DEFAULT_GRIPPER_TYPE,
+    camera_specs: Annotated[
+        list[str] | None,
+        typer.Option(
+            "--camera",
+            help=(
+                "Camera spec as name[:source_name][@HEIGHTxWIDTH]. Repeat for multiple cameras. "
+                "The name becomes the LeRobot output key (observation.images.<name>). "
+                "The optional source_name is the key in the source parquet (obs.frames.<source_name>.rgb.data); "
+                "if omitted, the image_ prefix is stripped from name to derive it. "
+                "Example: --camera head@256x256 --camera image_left_wrist:left_wrist@256x256"
+            ),
+        ),
+    ] = None,
+    image_batch_size: Annotated[
+        int, typer.Option(help="Batch size for image decoding. Example: --image-batch-size 32")
+    ] = DEFAULT_IMAGE_BATCH_SIZE,
+    per_robot_arm_dim: Annotated[
+        int, typer.Option(help="Per-robot arm joint/action dimension without gripper. Example: --per-robot-arm-dim 7")
+    ] = DEFAULT_PER_ROBOT_ARM_DIM,
+    success: Annotated[bool, typer.Option(help="Only include successful episodes. Example: --success")] = True,
+    n: Annotated[int, typer.Option(help="Maximum number of episodes to convert. -1 means all. Example: --n 50")] = -1,
+):
+    cameras = camera_specs_to_configs(camera_specs) if camera_specs is not None else list(DEFAULT_CAMERAS)
+    run_conversion(
+        root=output,
+        dataset_paths=dataset_paths or list(DEFAULT_DATASET_PATHS),
+        repo_id=repo_id,
+        robot_type=robot_type,
+        fps=fps,
+        robot_keys=robot_keys or list(DEFAULT_ROBOT_KEYS),
+        joints=joints,
+        gripper_type=gripper_type,
+        cameras=cameras,
+        image_batch_size=image_batch_size,
+        per_robot_arm_dim=per_robot_arm_dim,
+        success=success,
+        n=n,
+    )
+
+
 if __name__ == "__main__":
     app()
diff --git a/python/rcs/envs/configs.py b/python/rcs/envs/configs.py
index a4d46640..c649326e 100644
--- a/python/rcs/envs/configs.py
+++ b/python/rcs/envs/configs.py
@@ -21,7 +21,13 @@
 from rcs.envs.tasks import PickTaskConfig
 
 import rcs
-from rcs import CAMERA_PATHS, DEFAULT_TRANSFORMS, OBJECT_PATHS, SCENE_PATHS
+from rcs import (
+    CAMERA_PATHS,
+    DEFAULT_TRANSFORMS,
+    GRIPPER_OFFSETS,
+    OBJECT_PATHS,
+    SCENE_PATHS,
+)
 
 
 class EmptyWorldFR3(SimEnvCreator):
@@ -33,7 +39,7 @@ def config(self) -> SimEnvCreatorConfig:
         q_home[-1] = np.pi / 4
         robot_cfg = SimRobotConfig(
             robot_type=RobotType.FR3,
-            tcp_offset=rcs.common.Pose(pose_matrix=FrankaHandTCPOffset()),
+            tcp_offset=GRIPPER_OFFSETS[rcs.common.GripperType.FrankaHand],
             attachment_site=rcs.ROBOTS[RobotType.FR3].attachment_site,
             kinematic_model_path=rcs.ROBOTS[RobotType.FR3].mjcf_model_path,
             joint_rotational_tolerance=0.05 * (np.pi / 180.0),
@@ -178,6 +184,7 @@ class EmptyWorldFR3Duo(SimEnvCreator):
 
     def config(self) -> SimEnvCreatorConfig:
         robot_cfg = SimRobotConfig(
+            tcp_offset=GRIPPER_OFFSETS[rcs.common.GripperType("Robotiq2F85")],
             robot_type=RobotType.FR3,
             attachment_site=rcs.ROBOTS[RobotType.FR3].attachment_site,
             kinematic_model_path=rcs.ROBOTS[RobotType.FR3].mjcf_model_path,
@@ -357,7 +364,7 @@ def config(self) -> SimEnvCreatorConfig:
         lead_robot_name = self.lead_robot_name(cfg)
 
         robot_cfg = cfg.robot_cfgs[lead_robot_name]
-        robot_cfg.tcp_offset = rcs.common.Pose()
+        robot_cfg.tcp_offset = GRIPPER_OFFSETS[rcs.common.GripperType("Robotiq2F85")]
         robot_cfg.attachment_site = rcs.ROBOTS[rt].attachment_site
         robot_cfg.kinematic_model_path = rcs.ROBOTS[rt].mjcf_model_path
         robot_cfg.arm_collision_geoms = []
diff --git a/python/rcs/lerobot_joint_converter.py b/python/rcs/lerobot_joint_converter.py
new file mode 100644
index 00000000..a2819bf4
--- /dev/null
+++ b/python/rcs/lerobot_joint_converter.py
@@ -0,0 +1,432 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Iterable
+
+import duckdb
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import torch
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from rcs._core.common import GripperType, RobotType
+from torchvision.io import decode_jpeg
+from torchvision.transforms import v2
+
+import rcs
+
+DEFAULT_DATASET_PATHS = [
+    "data_grasp",
+]
+DEFAULT_HF_DATA_DIR = "data_lerobot_joint_simple"
+DEFAULT_REPO_ID = "rcs/grasp_joint_simple"
+DEFAULT_ROBOT_TYPE = "fr3"
+DEFAULT_FPS = 30
+DEFAULT_ROBOT_KEYS = ["left", "right"]
+DEFAULT_JOINTS = False
+DEFAULT_GRIPPER_TYPE = "Robotiq2F85"
+
+
+@dataclass(frozen=True)
+class CamConversionConfig:
+    name: str
+    resolution: tuple[int, int]
+    source_name: str | None = None
+
+    @property
+    def dataset_key(self) -> str:
+        return f"observation.images.{self.name}"
+
+    @property
+    def frame_name(self) -> str:
+        return self.source_name or self.name.removeprefix("image_")
+
+    @property
+    def image_column(self) -> str:
+        return f"image_{self.name}"
+
+
+DEFAULT_CAMERAS = [
+    CamConversionConfig(name="head", resolution=(256, 256)),
+    CamConversionConfig(name="image_left_wrist", source_name="left_wrist", resolution=(256, 256)),
+    CamConversionConfig(name="image_right_wrist", source_name="right_wrist", resolution=(256, 256)),
+]
+DEFAULT_IMAGE_BATCH_SIZE = 32
+DEFAULT_PER_ROBOT_ARM_DIM = 7
+
+
+def parse_camera_spec(spec: str) -> CamConversionConfig:
+    name_source, _, resolution_spec = spec.partition("@")
+    name, sep, source_name = name_source.partition(":")
+    if not name:
+        msg = f"Invalid camera spec '{spec}'"
+        raise ValueError(msg)
+
+    resolution = (256, 256)
+    if resolution_spec:
+        try:
+            height_str, width_str = resolution_spec.lower().split("x", maxsplit=1)
+            resolution = (int(height_str), int(width_str))
+        except ValueError as exc:
+            msg = f"Invalid camera resolution in spec '{spec}'"
+            raise ValueError(msg) from exc
+
+    return CamConversionConfig(
+        name=name,
+        source_name=source_name or None if sep else None,
+        resolution=resolution,
+    )
+
+
+def camera_specs_to_configs(camera_specs: Iterable[str]) -> list[CamConversionConfig]:
+    return [parse_camera_spec(spec) for spec in camera_specs]
+
+
+class JointDatasetConverter:
+    def __init__(
+        self,
+        root: str | Path,
+        robot_type: RobotType,
+        gripper_type: GripperType,
+        dataset_paths: list[str] | None = None,
+        repo_id: str = DEFAULT_REPO_ID,
+        fps: int = DEFAULT_FPS,
+        robot_keys: list[str] | None = None,
+        joints: bool = DEFAULT_JOINTS,
+        cameras: list[CamConversionConfig] | None = None,
+        image_batch_size: int = DEFAULT_IMAGE_BATCH_SIZE,
+        per_robot_arm_dim: int = DEFAULT_PER_ROBOT_ARM_DIM,
+    ):
+        self.root = Path(root)
+        self.conn = duckdb.connect()
+        self.dataset_paths = dataset_paths or list(DEFAULT_DATASET_PATHS)
+        self.repo_id = repo_id
+        self.robot_type = robot_type
+        self.fps = fps
+        self.robot_keys = robot_keys or list(DEFAULT_ROBOT_KEYS)
+        self.joints = joints
+        self.gripper_type = gripper_type
+        self.cameras = cameras or list(DEFAULT_CAMERAS)
+        self.image_batch_size = image_batch_size
+        self.per_robot_arm_dim = per_robot_arm_dim
+        self.per_robot_state_dim = self.per_robot_arm_dim + 1
+        self.state_dim = len(self.robot_keys) * self.per_robot_state_dim
+        self.source_sql = self._build_source_sql(self.dataset_paths)
+
+        self.tcp_offset = rcs.GRIPPER_OFFSETS[self.gripper_type]
+        self.ik = rcs.common.Pin(
+            rcs.ROBOTS[robot_type].mjcf_model_path,
+            rcs.ROBOTS[robot_type].attachment_site,
+        )
+        self.camera_resizers = {camera.name: v2.Resize(camera.resolution) for camera in self.cameras}
+
+        self.lrds = LeRobotDataset.create(
+            repo_id=self.repo_id,
+            robot_type=self.robot_type,
+            root=self.root,
+            fps=self.fps,
+            use_videos=False,
+            features=self._build_features(),
+            image_writer_threads=0,
+            image_writer_processes=0,
+        )
+
+    def _build_features(self) -> dict[str, dict[str, Any]]:
+        state_names = []
+        for robot_key in self.robot_keys:
+            state_names.extend([f"{robot_key}_joint_{i}" for i in range(self.per_robot_arm_dim)])
+            state_names.append(f"{robot_key}_gripper")
+
+        features = {
+            camera.dataset_key: {
+                "dtype": "image",
+                "shape": (*camera.resolution, 3),
+                "names": ["height", "width", "channel"],
+            }
+            for camera in self.cameras
+        }
+        features["observation.state"] = {
+            "dtype": "float32",
+            "shape": (self.state_dim,),
+            "names": state_names,
+        }
+        features["action"] = {
+            "dtype": "float32",
+            "shape": (self.state_dim,),
+            "names": state_names,
+        }
+        return features
+
+    def _build_source_sql(self, dataset_paths: list[str]) -> str:
+        queries = []
+        for path in dataset_paths:
+            escaped = str(path).replace("'", "''")
+            queries.append(f"SELECT * FROM read_parquet('{escaped}')")
+        return " UNION ALL ".join(queries)
+
+    def generate_examples(self, success: bool = True, n: int = -1):
+        uuids = self.conn.execute(f"SELECT DISTINCT uuid FROM ({self.source_sql}) AS src ORDER BY uuid").fetchall()
+
+        for (episode_id,) in uuids:
+            table = self._fetch_transition_table(episode_id)
+
+            converted = self.parse_episode(episode_id, table, success)
+            if converted:
+                n -= 1
+                if n == 0:
+                    break
+
+        self.lrds.finalize()
+
+    def _fetch_transition_table(self, episode_id: str) -> pd.DataFrame:
+        observation_selects = ",\n                    ".join(
+            [f"obs.{robot_key}.joints AS observation_joints_{robot_key}" for robot_key in self.robot_keys]
+            + [f"obs.{robot_key}.gripper AS observation_gripper_{robot_key}" for robot_key in self.robot_keys]
+        )
+        action_selects = ",\n                    ".join(
+            [f"info.{robot_key}.absolute_action AS absolute_action_{robot_key}" for robot_key in self.robot_keys]
+            + [f"env_action.{robot_key}.gripper AS action_gripper_{robot_key}" for robot_key in self.robot_keys]
+        )
+
+        return self.conn.execute(
+            f"""
+            SELECT
+                uuid,
+                step,
+                success,
+                instruction,
+                {observation_selects},
+                {action_selects}
+            FROM ({self.source_sql}) AS src
+            WHERE uuid = ?
+            ORDER BY step
+            """,
+            [episode_id],
+        ).df()
+
+    def _fetch_episode_success(self, episode_id: str) -> bool:
+        success = self.conn.execute(
+            f"SELECT COALESCE(MAX(success), FALSE) FROM ({self.source_sql}) AS src WHERE uuid = ?",
+            [episode_id],
+        ).fetchone()
+        assert success is not None
+        return bool(success[0])
+
+    def _image_query(self) -> str:
+        image_selects = ",\n                    ".join(
+            f"obs.frames.{camera.frame_name}.rgb.data AS {camera.image_column}" for camera in self.cameras
+        )
+        image_not_null_checks = "\n                  ".join(
+            f"AND obs.frames.{camera.frame_name}.rgb.data IS NOT NULL" for camera in self.cameras
+        )
+        image_columns = ",\n                ".join(camera.image_column for camera in self.cameras)
+
+        return f"""
+            WITH ordered AS (
+                SELECT
+                    uuid,
+                    step,
+                    {image_selects}
+                FROM ({self.source_sql}) AS src
+                WHERE uuid = ?
+                  {image_not_null_checks}
+            )
+            SELECT
+                step,
+                {image_columns}
+            FROM ordered
+            ORDER BY step
+        """
+
+    def _is_missing(self, value: object) -> bool:
+        if value is None or value is pd.NA:
+            return True
+        if isinstance(value, float):
+            return bool(np.isnan(value))
+        return False
+
+    def _build_observation_state(self, row: pd.Series) -> np.ndarray:
+        vectors = []
+        for robot_key in self.robot_keys:
+            joints = row[f"observation_joints_{robot_key}"]
+            gripper = row[f"observation_gripper_{robot_key}"]
+            if self._is_missing(joints) or self._is_missing(gripper):
+                msg = f"Missing observation state for robot '{robot_key}' at step {row['step']}"
+                raise ValueError(msg)
+
+            joints_vec = np.asarray(joints, dtype=np.float32)
+            gripper_vec = np.asarray(gripper, dtype=np.float32)
+            if joints_vec.shape != (self.per_robot_arm_dim,) or gripper_vec.shape != (1,):
+                msg = (
+                    f"Unexpected observation shapes for robot '{robot_key}' at step {row['step']}: "
+                    f"joints={joints_vec.shape}, gripper={gripper_vec.shape}"
+                )
+                raise ValueError(msg)
+            vectors.append(np.concatenate([joints_vec, gripper_vec]).astype(np.float32))
+
+        return np.concatenate(vectors).astype(np.float32)
+
+    def _convert_action_to_joint_space(self, row: pd.Series) -> np.ndarray:
+        actions = []
+        for robot_key in self.robot_keys:
+            observation_joints = row[f"observation_joints_{robot_key}"]
+            absolute_action = row[f"absolute_action_{robot_key}"]
+            action_gripper = row[f"action_gripper_{robot_key}"]
+            if (
+                self._is_missing(observation_joints)
+                or self._is_missing(absolute_action)
+                or self._is_missing(action_gripper)
+            ):
+                msg = f"Missing action inputs for robot '{robot_key}' at step {row['step']}"
+                raise ValueError(msg)
+
+            observation_joints_vec = np.asarray(observation_joints, dtype=np.float64)
+            absolute_action_vec = np.asarray(absolute_action, dtype=np.float64)
+            action_gripper_vec = np.asarray(action_gripper, dtype=np.float32)
+            if (
+                observation_joints_vec.shape != (self.per_robot_arm_dim,)
+                or absolute_action_vec.shape != (self.per_robot_arm_dim,)
+                or action_gripper_vec.shape != (1,)
+            ):
+                msg = (
+                    f"Unexpected action shapes for robot '{robot_key}' at step {row['step']}: "
+                    f"observation_joints={observation_joints_vec.shape}, "
+                    f"absolute_action={absolute_action_vec.shape}, action_gripper={action_gripper_vec.shape}"
+                )
+                raise ValueError(msg)
+
+            if self.joints:
+                arm_action_vec = absolute_action_vec.astype(np.float32)
+            else:
+                target_pose = rcs.common.Pose(
+                    translation=absolute_action_vec[:3],
+                    quaternion=absolute_action_vec[3:7],
+                )
+                ik_joints: np.ndarray | None = self.ik.inverse(
+                    target_pose, observation_joints_vec, tcp_offset=self.tcp_offset
+                )
+                if ik_joints is None:
+                    msg = f"IK failed for robot '{robot_key}' at step {row['step']}"
+                    raise ValueError(msg)
+                arm_action_vec = np.asarray(ik_joints, dtype=np.float32)
+
+            actions.append(np.concatenate([arm_action_vec, action_gripper_vec]).astype(np.float32))
+
+        concatenated = np.concatenate(actions).astype(np.float32)
+        if concatenated.shape != (self.state_dim,):
+            msg = f"Unexpected concatenated action shape {concatenated.shape} at step {row['step']}"
+            raise ValueError(msg)
+        return concatenated
+
+    def _prepare_transition_table(self, table: pd.DataFrame) -> pd.DataFrame:
+        if len(table) == 0:
+            return table
+
+        df = table.copy()  # noqa: PD901
+        df["observation_state"] = df.apply(self._build_observation_state, axis=1)
+        df["action_vector"] = df.apply(self._convert_action_to_joint_space, axis=1)
+
+        prev_action: np.ndarray | None = None
+        keep_mask = []
+        for action_vec in df["action_vector"]:
+            assert isinstance(action_vec, np.ndarray)
+            keep_mask.append(prev_action is None or not np.array_equal(action_vec, prev_action))
+            prev_action = action_vec
+
+        return df.loc[keep_mask].reset_index(drop=True)
+
+    def parse_episode(self, episode_id: str, table: pd.DataFrame, success: bool):
+        table = self._prepare_transition_table(table)
+        if len(table) == 0:
+            return False
+
+        if success and not self._fetch_episode_success(episode_id):
+            return False
+
+        df = table.reset_index(drop=True)  # noqa: PD901
+        rows_by_step = {int(row["step"]): row for _, row in df.iterrows()}
+        step_order = [int(step) for step in df["step"].tolist()]
+        frames_by_step: dict[int, dict[str, np.ndarray]] = {}
+
+        reader = self.conn.execute(self._image_query(), [episode_id]).fetch_record_batch(
+            rows_per_batch=self.image_batch_size
+        )
+        for batch in reader:
+            self._decode_image_batch(batch, frames_by_step)
+
+        num_frames_added = 0
+        for step in step_order:
+            curr = rows_by_step[step]
+            if step not in frames_by_step:
+                continue
+            images = frames_by_step[step]
+
+            frame: dict[str, Any] = {camera.dataset_key: images[camera.name] for camera in self.cameras}
+            frame["observation.state"] = curr["observation_state"]
+            frame["action"] = curr["action_vector"]
+            frame["task"] = str(curr["instruction"])
+
+            self.lrds.add_frame(frame)
+            num_frames_added += 1
+
+        if num_frames_added == 0:
+            return False
+        self.lrds.save_episode()
+        return True
+
+    def _decode_and_resize_batch(self, image_bytes_list: list[bytes], camera: CamConversionConfig) -> np.ndarray:
+        image_tensors = [
+            torch.frombuffer(bytearray(image_bytes), dtype=torch.uint8) for image_bytes in image_bytes_list
+        ]
+        decoded = decode_jpeg(image_tensors)
+        batch = torch.stack(decoded)
+        resized = self.camera_resizers[camera.name](batch)
+        return resized.permute(0, 2, 3, 1).cpu().numpy()
+
+    def _decode_image_batch(self, batch: pa.RecordBatch, frames_by_step: dict[int, dict[str, np.ndarray]]) -> None:
+        batch_dict = batch.to_pydict()
+        steps = [int(step) for step in batch_dict["step"]]
+        decoded_images = {}
+        for camera in self.cameras:
+            decoded_images[camera.name] = self._decode_and_resize_batch(batch_dict[camera.image_column], camera)
+
+        for idx, step in enumerate(steps):
+            frames_by_step[step] = {camera.name: decoded_images[camera.name][idx] for camera in self.cameras}
+
+
+def run_conversion(
+    root: str | Path = DEFAULT_HF_DATA_DIR,
+    dataset_paths: list[str] | None = None,
+    repo_id: str = DEFAULT_REPO_ID,
+    robot_type: str = DEFAULT_ROBOT_TYPE,
+    fps: int = DEFAULT_FPS,
+    robot_keys: list[str] | None = None,
+    joints: bool = DEFAULT_JOINTS,
+    gripper_type: str = DEFAULT_GRIPPER_TYPE,
+    cameras: list[CamConversionConfig] | None = None,
+    image_batch_size: int = DEFAULT_IMAGE_BATCH_SIZE,
+    per_robot_arm_dim: int = DEFAULT_PER_ROBOT_ARM_DIM,
+    success: bool = True,
+    n: int = -1,
+) -> None:
+    robot_type_converted = RobotType(robot_type)
+    gripper_type_converted = GripperType(gripper_type)
+    converter = JointDatasetConverter(
+        root=root,
+        robot_type=robot_type_converted,
+        gripper_type=gripper_type_converted,
+        dataset_paths=dataset_paths,
+        repo_id=repo_id,
+        fps=fps,
+        robot_keys=robot_keys,
+        joints=joints,
+        cameras=cameras,
+        image_batch_size=image_batch_size,
+        per_robot_arm_dim=per_robot_arm_dim,
+    )
+    converter.generate_examples(success=success, n=n)
+
+
+if __name__ == "__main__":
+    run_conversion()