demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "970e2b9e-ccce-47b2-bccc-9471841cce44",
   "metadata": {},
   "source": [
    "## Installation required in Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tckkiSjNY0Is",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tckkiSjNY0Is",
    "outputId": "627b5bff-c792-4954-947f-40baab0de93c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'diffusion_policy_quadrotor'...\n",
      "remote: Enumerating objects: 155, done.\u001b[K\n",
      "remote: Counting objects: 100% (155/155), done.\u001b[K\n",
      "remote: Compressing objects: 100% (104/104), done.\u001b[K\n",
      "remote: Total 155 (delta 66), reused 111 (delta 38), pack-reused 0\u001b[K\n",
      "Receiving objects: 100% (155/155), 4.19 MiB | 25.69 MiB/s, done.\n",
      "Resolving deltas: 100% (66/66), done.\n"
     ]
    }
   ],
   "source": [
    "! git clone https://github.com/shaoanlu/diffusion_policy_quadrotor.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7rrgEckSZEdx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7rrgEckSZEdx",
    "outputId": "5cd7bcf5-5b4c-4a48-996b-7257cdf7f03e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/diffusion_policy_quadrotor\n",
      "\u001b[0m\u001b[01;34massets\u001b[0m/  \u001b[01;34mconfig\u001b[0m/  \u001b[01;34mcore\u001b[0m/  demo.ipynb  LICENSE  pyproject.toml  README.md  \u001b[01;34mutils\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "%cd diffusion_policy_quadrotor\n",
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "rK2Hjrq_Y4T6",
   "metadata": {
    "id": "rK2Hjrq_Y4T6"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip3 install torch==1.13.1 torchvision==0.14.1 diffusers==0.18.2 jax==0.4.23 jaxlib==0.4.23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7a0434-3333-4502-9563-adcc12d4a413",
   "metadata": {
    "id": "ee7a0434-3333-4502-9563-adcc12d4a413"
   },
   "source": [
    "## Description\n",
    "\n",
    "This notebook demonstrate using a diffusion policy controller to drive a quadrotor moving from (0, 0) to (5, 5) with random circle obstacles presented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fffc0bb0-584b-4602-885f-a1cfa06d21b6",
   "metadata": {
    "id": "fffc0bb0-584b-4602-885f-a1cfa06d21b6"
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "670f6c7f-93c9-47dc-b2e4-81ea8949b3f9",
   "metadata": {
    "id": "670f6c7f-93c9-47dc-b2e4-81ea8949b3f9"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import torch\n",
    "import yaml\n",
    "import collections\n",
    "from tqdm.auto import tqdm\n",
    "import gdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa8f1de5-924a-4889-aacc-bac7601012c5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87,
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   "outputs": [],
   "source": [
    "from utils.normalizers import LinearNormalizer\n",
    "from core.controllers.quadrotor_diffusion_policy import QuadrotorDiffusionPolicy, build_networks_from_config, build_noise_scheduler_from_config\n",
    "from core.controllers.quadrotor_clf_cbf_qp import QuadrotorCLFCBFController\n",
    "from core.env.planar_quadrotor import PlanarQuadrotorEnv\n",
    "from utils.visualization import visualize_quadrotor_simulation_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06cf3d67-3f96-457e-a634-77b6e5f2e31e",
   "metadata": {
    "id": "06cf3d67-3f96-457e-a634-77b6e5f2e31e"
   },
   "source": [
    "## Load the config file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c8c8f90-8ac2-4f87-943d-e41b7ff9ff6a",
   "metadata": {
    "id": "0c8c8f90-8ac2-4f87-943d-e41b7ff9ff6a",
    "scrolled": true
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   "outputs": [],
   "source": [
    "with open(\"config/config.yaml\", \"r\") as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "# Whether to use a finetuned model trained following tricks mentioned in\n",
    "# [Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think](https://arxiv.org/abs/2409.11355)\n",
    "use_single_step_inference = config.get(\"controller\").get(\"common\").get(\"use_single_step_inference\", False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd56c89a-520f-46b5-b9d6-ebef0160b184",
   "metadata": {
    "id": "dd56c89a-520f-46b5-b9d6-ebef0160b184"
   },
   "source": [
    "## Instantiate the controller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb0707a7-3d66-46da-b722-2b2f42d8e099",
   "metadata": {
    "id": "cb0707a7-3d66-46da-b722-2b2f42d8e099"
   },
   "outputs": [],
   "source": [
    "clf_cbf_controller = QuadrotorCLFCBFController(config=config)\n",
    "\n",
    "controller = QuadrotorDiffusionPolicy(\n",
    "    model=build_networks_from_config(config),\n",
    "    noise_scheduler=build_noise_scheduler_from_config(config),\n",
    "    normalizer=LinearNormalizer(),\n",
    "    clf_cbf_controller=None,  # set as clf_cbf_controller to enable CLF-CBF traj refinement\n",
    "    config=config,\n",
    "    device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac28d88-e304-46b3-bc28-5fc54cd3705d",
   "metadata": {
    "id": "cac28d88-e304-46b3-bc28-5fc54cd3705d"
   },
   "source": [
    "## Download and load pretrained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "727e3937-dd21-46a2-9fff-c41c1d653022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# download pretrained weights from Google drive\n",
    "if use_single_step_inference:\n",
    "    ckpts_path = \"ema_noise_pred_net2_ph96_oh2_ah10_v8_singlestepFT.ckpt\"\n",
    "    if not os.path.isfile(ckpts_path):\n",
    "        gdown.download(id=\"1UhxlzoQ6DOt0HZhokzU4ktl2MfzlA2Ii\", output=ckpts_path, quiet=False)\n",
    "else:\n",
    "    ckpts_path = \"ema_noise_pred_net2_ph96_oh2_ah10_v8.ckpt\"\n",
    "    if not os.path.isfile(ckpts_path):\n",
    "        gdown.download(id=\"1-as6EqMLECxU7IVLZZIEDAcXMox_RkI_\", output=ckpts_path, quiet=False)\n",
    "\n",
    "# load weights\n",
    "controller.load_weights(ckpts_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0fda0f4-f5cb-4fa9-86da-abf991edfb4b",
   "metadata": {
    "id": "a0fda0f4-f5cb-4fa9-86da-abf991edfb4b"
   },
   "source": [
    "## Instantiate the simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b0ae93e-8fe6-40af-92e6-12643aa21f6d",
   "metadata": {
    "id": "6b0ae93e-8fe6-40af-92e6-12643aa21f6d"
   },
   "outputs": [],
   "source": [
    "sim = PlanarQuadrotorEnv(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a679998d-7a2e-48dc-b0a4-30c24314e307",
   "metadata": {
    "id": "a679998d-7a2e-48dc-b0a4-30c24314e307"
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   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1f1c50bc-e529-432f-92a9-9bb3c6e9fdf7",
   "metadata": {
    "id": "1f1c50bc-e529-432f-92a9-9bb3c6e9fdf7"
   },
   "source": [
    "## Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eecaed67-9814-4a62-aa7f-2caf5d5b43aa",
   "metadata": {
    "id": "eecaed67-9814-4a62-aa7f-2caf5d5b43aa"
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   "outputs": [],
   "source": [
    "def generate_random_obstacles():\n",
    "    num_obstacles = np.random.randint(1, 8)\n",
    "    obs_center, obs_radius = np.empty((num_obstacles, 2)), np.ones((num_obstacles,))\n",
    "    for obs_idx in range(num_obstacles):  # set XY position of each obstacle\n",
    "        obs_center[obs_idx, ...] = np.random.randint(1, 8, size=(2,))\n",
    "        obs_radius[obs_idx] = np.random.uniform(0.2, 1.5)\n",
    "    return obs_center, obs_radius\n",
    "\n",
    "def encode_obstacle_info(obs_center: np.ndarray, obs_radius: np.ndarray):\n",
    "    obs_encode = np.zeros((7, 7))\n",
    "    for center, radius in zip(obs_center, obs_radius):\n",
    "        obs_encode[tuple((center-1).astype(np.int32))] = radius\n",
    "    obs_encode = obs_encode.flatten()\n",
    "    return obs_encode"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d93b38cf8264ebe80428df1989880d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Eval:   0%|          | 0/400 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "\n",
    "# Env parameters\n",
    "max_steps = 400\n",
    "dt = 0.01\n",
    "ratio_sim_ts = 10  # ratio of sampling time between the simulator and the controller\n",
    "\n",
    "# get first observation\n",
    "state = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])  # [y, y_dot, z, z_dot, phi, phi_dot]\n",
    "states = [state]  # `states` is a list containing the states over time\n",
    "controls = [np.array([0, 0])]\n",
    "obs_center, obs_radius = generate_random_obstacles()\n",
    "obs_encode = encode_obstacle_info(obs_center, obs_radius)\n",
    "obs = {\n",
    "    \"state\": collections.deque([state] * controller.obs_horizon, maxlen=controller.obs_horizon),\n",
    "    \"obs_encode\": [obs_encode],\n",
    "    \"obs_center\": obs_center,\n",
    "    \"obs_radius\": obs_radius,\n",
    "}\n",
    "\n",
    "# termimnation params\n",
    "done = False\n",
    "step_idx = 0\n",
    "\n",
    "with tqdm(total=max_steps, desc=\"Eval\") as pbar:\n",
    "    while not done:\n",
    "        # controller inference\n",
    "        action = controller.predict_action(obs)\n",
    "\n",
    "        # execute action_horizon steps without replanning\n",
    "        for i in range(action.shape[0]):\n",
    "            # stepping env\n",
    "            command = controller.calculate_force_command(state, action[i])\n",
    "            for _ in range(ratio_sim_ts):\n",
    "                state = sim.step(state, command, dt=dt/ratio_sim_ts)\n",
    "            # save observations and controls\n",
    "            obs[\"state\"].append(state.copy())\n",
    "            states.append(state.copy())\n",
    "            controls.append(action[i].copy())\n",
    "\n",
    "            # update progress bar\n",
    "            step_idx += 1\n",
    "            pbar.update(1)\n",
    "            if step_idx > max_steps:\n",
    "                done = True\n",
    "            if done:\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b006ad91-e853-4a03-80c5-7ecb142bc475",
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   "source": []
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  {
   "cell_type": "markdown",
   "id": "602bdd60-2b90-48db-a29b-7f8645501142",
   "metadata": {
    "id": "602bdd60-2b90-48db-a29b-7f8645501142"
   },
   "source": [
    "## Visualize result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b92bcec2-d95d-4361-9b92-991346d83950",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
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    "outputId": "0d8c5a83-8002-4deb-a87b-48a04af6d0af"
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = np.array(states)\n",
    "visualize_quadrotor_simulation_result(sim, states, obs_center=obs_center, obs_radius=obs_radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33740290-22ae-45bd-822f-0bea782691bd",
   "metadata": {
    "id": "33740290-22ae-45bd-822f-0bea782691bd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f15cae17-f337-4ba0-a4ea-5a10a8bc0390",
   "metadata": {
    "id": "f15cae17-f337-4ba0-a4ea-5a10a8bc0390"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f889988-1fef-4c11-b113-457247db185c",
   "metadata": {
    "id": "7f889988-1fef-4c11-b113-457247db185c"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c503b86-5211-4403-a830-b8a100e73b97",
   "metadata": {
    "id": "6c503b86-5211-4403-a830-b8a100e73b97"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0772564-5e68-4884-a266-f5467db4a433",
   "metadata": {
    "id": "f0772564-5e68-4884-a266-f5467db4a433"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c1ad555-40ce-4917-b0f7-e26dce4d485d",
   "metadata": {
    "id": "3c1ad555-40ce-4917-b0f7-e26dce4d485d"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
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