Loading files
This file is pinned to the selected release. Download the original to open it in your design tool.
b47e70849This file is pinned to the selected release. Download the original to open it in your design tool.
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "MpkYHwCqk7W-"
},
"source": [
"\n",
"\n",
"# <h1><center>Tutorial <a href=\"https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" width=\"140\" align=\"center\"/></a></center></h1>\n",
"\n",
"This notebook provides an introductory tutorial for [**MuJoCo XLA (MJX)**](https://github.com/google-deepmind/mujoco/blob/main/mjx), a JAX-based implementation of MuJoCo useful for RL training workloads.\n",
"\n",
"**A Colab runtime with GPU acceleration is required.** If you're using a CPU-only runtime, you can switch using the menu \"Runtime > Change runtime type\".\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xBSdkbmGN2K-"
},
"source": [
"### Copyright notice"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_UbO9uhtBSX5"
},
"source": [
"> <p><small><small>Copyright 2023 DeepMind Technologies Limited.</small></p>\n",
"> <p><small><small>Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at <a href=\"http://www.apache.org/licenses/LICENSE-2.0\">http://www.apache.org/licenses/LICENSE-2.0</a>.</small></small></p>\n",
"> <p><small><small>Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.</small></small></p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YvyGCsgSCxHQ"
},
"source": [
"# Install MuJoCo, MJX, and Brax"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "Xqo7pyX-n72M"
},
"outputs": [],
"source": [
"!pip install mujoco\n",
"!pip install mujoco_mjx\n",
"!pip install brax\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"id": "IbZxYDxzoz5R"
},
"outputs": [],
"source": [
"#@title Check if MuJoCo installation was successful\n",
"\n",
"from google.colab import files\n",
"\n",
"import distutils.util\n",
"import os\n",
"import subprocess\n",
"if subprocess.run('nvidia-smi').returncode:\n",
" raise RuntimeError(\n",
" 'Cannot communicate with GPU. '\n",
" 'Make sure you are using a GPU Colab runtime. '\n",
" 'Go to the Runtime menu and select Choose runtime type.')\n",
"\n",
"# Add an ICD config so that glvnd can pick up the Nvidia EGL driver.\n",
"# This is usually installed as part of an Nvidia driver package, but the Colab\n",
"# kernel doesn't install its driver via APT, and as a result the ICD is missing.\n",
"# (https://github.com/NVIDIA/libglvnd/blob/master/src/EGL/icd_enumeration.md)\n",
"NVIDIA_ICD_CONFIG_PATH = '/usr/share/glvnd/egl_vendor.d/10_nvidia.json'\n",
"if not os.path.exists(NVIDIA_ICD_CONFIG_PATH):\n",
" with open(NVIDIA_ICD_CONFIG_PATH, 'w') as f:\n",
" f.write(\"\"\"{\n",
" \"file_format_version\" : \"1.0.0\",\n",
" \"ICD\" : {\n",
" \"library_path\" : \"libEGL_nvidia.so.0\"\n",
" }\n",
"}\n",
"\"\"\")\n",
"\n",
"# Configure MuJoCo to use the EGL rendering backend (requires GPU)\n",
"print('Setting environment variable to use GPU rendering:')\n",
"%env MUJOCO_GL=egl\n",
"\n",
"try:\n",
" print('Checking that the installation succeeded:')\n",
" import mujoco\n",
" mujoco.MjModel.from_xml_string('<mujoco/>')\n",
"except Exception as e:\n",
" raise e from RuntimeError(\n",
" 'Something went wrong during installation. Check the shell output above '\n",
" 'for more information.\\n'\n",
" 'If using a hosted Colab runtime, make sure you enable GPU acceleration '\n",
" 'by going to the Runtime menu and selecting \"Choose runtime type\".')\n",
"\n",
"print('Installation successful.')\n",
"\n",
"# Tell XLA to use Triton GEMM, this improves steps/sec by ~30% on some GPUs\n",
"xla_flags = os.environ.get('XLA_FLAGS', '')\n",
"xla_flags += ' --xla_gpu_triton_gemm_any=True'\n",
"os.environ['XLA_FLAGS'] = xla_flags\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"id": "T5f4w3Kq2X14"
},
"outputs": [],
"source": [
"#@title Import packages for plotting and creating graphics\n",
"import time\n",
"import itertools\n",
"import numpy as np\n",
"from typing import Callable, NamedTuple, Optional, Union, List\n",
"\n",
"# Graphics and plotting.\n",
"print('Installing mediapy:')\n",
"!command -v ffmpeg >/dev/null || (apt update && apt install -y ffmpeg)\n",
"!pip install -q mediapy\n",
"import mediapy as media\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# More legible printing from numpy.\n",
"np.set_printoptions(precision=3, suppress=True, linewidth=100)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"id": "ObF1UXrkb0Nd"
},
"outputs": [],
"source": [
"#@title Import MuJoCo, MJX, and Brax\n",
"from datetime import datetime\n",
"from etils import epath\n",
"import functools\n",
"from IPython.display import HTML\n",
"from typing import Any, Dict, Sequence, Tuple, Union\n",
"import os\n",
"from ml_collections import config_dict\n",
"\n",
"\n",
"import jax\n",
"from jax import numpy as jp\n",
"import numpy as np\n",
"from flax.training import orbax_utils\n",
"from flax import struct\n",
"from matplotlib import pyplot as plt\n",
"import mediapy as media\n",
"from orbax import checkpoint as ocp\n",
"\n",
"import mujoco\n",
"from mujoco import mjx\n",
"\n",
"from brax import base\n",
"from brax import envs\n",
"from brax import math\n",
"from brax.base import Base, Motion, Transform\n",
"from brax.base import State as PipelineState\n",
"from brax.envs.base import Env, PipelineEnv, State\n",
"from brax.mjx.base import State as MjxState\n",
"from brax.training.agents.ppo import train as ppo\n",
"from brax.training.agents.ppo import networks as ppo_networks\n",
"from brax.io import html, mjcf, model\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nj4-Xmx4DFaq"
},
"source": [
"# Introduction to MJX\n",
"\n",
"MJX is an implementation of MuJoCo written in [JAX](https://jax.readthedocs.io/en/latest/index.html), enabling large batch training on GPU/TPU. In this notebook, we will demonstrate how to train RL policies with MJX.\n",
"\n",
"Before we get into hefty RL workloads, let's get started with a simpler example! The entrypoint into MJX is through MuJoCo, so first we load a MuJoCo model:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "bNus3mbbDz6a"
},
"outputs": [],
"source": [
"xml = \"\"\"\n",
"<mujoco>\n",
" <worldbody>\n",
" <light name=\"top\" pos=\"0 0 1\"/>\n",
" <body name=\"box_and_sphere\" euler=\"0 0 -30\">\n",
" <joint name=\"swing\" type=\"hinge\" axis=\"1 -1 0\" pos=\"-.2 -.2 -.2\"/>\n",
" <geom name=\"red_box\" type=\"box\" size=\".2 .2 .2\" rgba=\"1 0 0 1\"/>\n",
" <geom name=\"green_sphere\" pos=\".2 .2 .2\" size=\".1\" rgba=\"0 1 0 1\"/>\n",
" </body>\n",
" </worldbody>\n",
"</mujoco>\n",
"\"\"\"\n",
"\n",
"# Make model, data, and renderer\n",
"mj_model = mujoco.MjModel.from_xml_string(xml)\n",
"mj_data = mujoco.MjData(mj_model)\n",
"renderer = mujoco.Renderer(mj_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Po5oykJbFQbj"
},
"source": [
"Next we take the MuJoCo model and data, and place them on the GPU device using MJX."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "TSpoOWqeEC3P"
},
"outputs": [],
"source": [
"mjx_model = mjx.put_model(mj_model)\n",
"mjx_data = mjx.put_data(mj_model, mj_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6rxMMSs4OJJf"
},
"source": [
"Below, we print the `qpos` from MuJoCo and MJX. Notice that the `qpos` for the mjData is a numpy array living on the CPU, while the `qpos` for `mjx.Data` is a JAX Array living on the GPU device."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "ZOD582pfOLP-"
},
"outputs": [],
"source": [
"print(mj_data.qpos, type(mj_data.qpos))\n",
"print(mjx_data.qpos, type(mjx_data.qpos), mjx_data.qpos.devices())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZShF9-o_JLm3"
},
"source": [
"Let's run the simulation in MuJoCo and render the trajectory. This example is taken from the [MuJoCo tutorial](https://colab.sandbox.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "HDlPlX05I3m-"
},
"outputs": [],
"source": [
"# enable joint visualization option:\n",
"scene_option = mujoco.MjvOption()\n",
"scene_option.flags[mujoco.mjtVisFlag.mjVIS_JOINT] = True\n",
"\n",
"duration = 3.8 # (seconds)\n",
"framerate = 60 # (Hz)\n",
"\n",
"frames = []\n",
"mujoco.mj_resetData(mj_model, mj_data)\n",
"while mj_data.time < duration:\n",
" mujoco.mj_step(mj_model, mj_data)\n",
" if len(frames) < mj_data.time * framerate:\n",
" renderer.update_scene(mj_data, scene_option=scene_option)\n",
" pixels = renderer.render()\n",
" frames.append(pixels)\n",
"\n",
"# Simulate and display video.\n",
"media.show_video(frames, fps=framerate)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m70b_RxBJOyd"
},
"source": [
"Now let's run the same exact simulation on the GPU device using MJX!\n",
"\n",
"In the example below, we use `mjx.step` instead of `mujoco.mj_step`, and we also [`jax.jit`](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html) the `mjx.step` so that it runs efficiently on the GPU. For each frame, we convert the `mjx.Data` back to `mjData` so that we can use the MuJoCo renderer.\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "Pr29xq0-JRQv"
},
"outputs": [],
"source": [
"\n",
"jit_step = jax.jit(mjx.step)\n",
"\n",
"frames = []\n",
"mujoco.mj_resetData(mj_model, mj_data)\n",
"mjx_data = mjx.put_data(mj_model, mj_data)\n",
"while mjx_data.time < duration:\n",
" mjx_data = jit_step(mjx_model, mjx_data)\n",
" if len(frames) < mjx_data.time * framerate:\n",
" mj_data = mjx.get_data(mj_model, mjx_data)\n",
" renderer.update_scene(mj_data, scene_option=scene_option)\n",
" pixels = renderer.render()\n",
" frames.append(pixels)\n",
"\n",
"media.show_video(frames, fps=framerate)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wXsQ4qO2KO3Q"
},
"source": [
"Running single threaded physics simulation on the GPU is not very [efficient](https://mujoco.readthedocs.io/en/stable/mjx.html#mjx-the-sharp-bits). The advantage with MJX is that we can run environments in parallel on a hardware accelerated device. Let's try it out!\n",
"\n",
"In the example below, we create 4096 copies of the `mjx.Data` and we run the `mjx.step` over the batched data. Since MJX is implemented in JAX, we take advantage of [`jax.vmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.vmap.html) to run the `mjx.step` in parallel over all `mjx.Data`."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "rrdrcKRVK6w9"
},
"outputs": [],
"source": [
"rng = jax.random.PRNGKey(0)\n",
"rng = jax.random.split(rng, 4096)\n",
"batch = jax.vmap(lambda rng: mjx_data.replace(qpos=jax.random.uniform(rng, (1,))))(rng)\n",
"\n",
"jit_step = jax.jit(jax.vmap(mjx.step, in_axes=(None, 0)))\n",
"batch = jit_step(mjx_model, batch)\n",
"\n",
"print(batch.qpos)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x4lL220cOj0q"
},
"source": [
"We can copy the batched `mjx.Data` back to MuJoCo like we did before:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "Jtz7j1PDOnw5"
},
"outputs": [],
"source": [
"batched_mj_data = mjx.get_data(mj_model, batch)\n",
"print([d.qpos for d in batched_mj_data])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RAv6WUVUm78k"
},
"source": [
"# Training a Policy with MJX\n",
"\n",
"Running large batch physics simulation is useful for training RL policies. Here we demonstrate training RL policies with MJX using the RL library from [Brax](https://github.com/google/brax).\n",
"\n",
"Below, we implement the classic Humanoid environment using MJX and Brax. We inherit from the `MjxEnv` implementation in Brax so that we can step the physics with MJX while training with Brax RL implementations.\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "mtGMYNLE3QJN"
},
"outputs": [],
"source": [
"#@title Humanoid Env\n",
"\n",
"HUMANOID_ROOT_PATH = epath.Path(epath.resource_path('mujoco')) / 'mjx/test_data/humanoid'\n",
"\n",
"class Humanoid(PipelineEnv):\n",
"\n",
" def __init__(\n",
" self,\n",
" forward_reward_weight=1.25,\n",
" ctrl_cost_weight=0.1,\n",
" healthy_reward=5.0,\n",
" terminate_when_unhealthy=True,\n",
" healthy_z_range=(1.0, 2.0),\n",
" reset_noise_scale=1e-2,\n",
" exclude_current_positions_from_observation=True,\n",
" **kwargs,\n",
" ):\n",
"#\n",
" mj_model = mujoco.MjModel.from_xml_path(\n",
" (HUMANOID_ROOT_PATH / 'humanoid.xml').as_posix())\n",
" mj_model.opt.solver = mujoco.mjtSolver.mjSOL_CG\n",
" mj_model.opt.iterations = 6\n",
" mj_model.opt.ls_iterations = 6\n",
"\n",
" sys = mjcf.load_model(mj_model)\n",
"\n",
" physics_steps_per_control_step = 5\n",
" kwargs['n_frames'] = kwargs.get(\n",
" 'n_frames', physics_steps_per_control_step)\n",
" kwargs['backend'] = 'mjx'\n",
"\n",
" super().__init__(sys, **kwargs)\n",
"\n",
" self._forward_reward_weight = forward_reward_weight\n",
" self._ctrl_cost_weight = ctrl_cost_weight\n",
" self._healthy_reward = healthy_reward\n",
" self._terminate_when_unhealthy = terminate_when_unhealthy\n",
" self._healthy_z_range = healthy_z_range\n",
" self._reset_noise_scale = reset_noise_scale\n",
" self._exclude_current_positions_from_observation = (\n",
" exclude_current_positions_from_observation\n",
" )\n",
"\n",
" def reset(self, rng: jp.ndarray) -> State:\n",
" \"\"\"Resets the environment to an initial state.\"\"\"\n",
" rng, rng1, rng2 = jax.random.split(rng, 3)\n",
"\n",
" low, hi = -self._reset_noise_scale, self._reset_noise_scale\n",
" qpos = self.sys.qpos0 + jax.random.uniform(\n",
" rng1, (self.sys.nq,), minval=low, maxval=hi\n",
" )\n",
" qvel = jax.random.uniform(\n",
" rng2, (self.sys.nv,), minval=low, maxval=hi\n",
" )\n",
"\n",
" data = self.pipeline_init(qpos, qvel)\n",
"\n",
" obs = self._get_obs(data, jp.zeros(self.sys.nu))\n",
" reward, done, zero = jp.zeros(3)\n",
" metrics = {\n",
" 'forward_reward': zero,\n",
" 'reward_linvel': zero,\n",
" 'reward_quadctrl': zero,\n",
" 'reward_alive': zero,\n",
" 'x_position': zero,\n",
" 'y_position': zero,\n",
" 'distance_from_origin': zero,\n",
" 'x_velocity': zero,\n",
" 'y_velocity': zero,\n",
" }\n",
" return State(data, obs, reward, done, metrics)\n",
"\n",
" def step(self, state: State, action: jp.ndarray) -> State:\n",
" \"\"\"Runs one timestep of the environment's dynamics.\"\"\"\n",
" data0 = state.pipeline_state\n",
" data = self.pipeline_step(data0, action)\n",
"\n",
" com_before = data0.subtree_com[1]\n",
" com_after = data.subtree_com[1]\n",
" velocity = (com_after - com_before) / self.dt\n",
" forward_reward = self._forward_reward_weight * velocity[0]\n",
"\n",
" min_z, max_z = self._healthy_z_range\n",
" is_healthy = jp.where(data.q[2] < min_z, 0.0, 1.0)\n",
" is_healthy = jp.where(data.q[2] > max_z, 0.0, is_healthy)\n",
" if self._terminate_when_unhealthy:\n",
" healthy_reward = self._healthy_reward\n",
" else:\n",
" healthy_reward = self._healthy_reward * is_healthy\n",
"\n",
" ctrl_cost = self._ctrl_cost_weight * jp.sum(jp.square(action))\n",
"\n",
" obs = self._get_obs(data, action)\n",
" reward = forward_reward + healthy_reward - ctrl_cost\n",
" done = 1.0 - is_healthy if self._terminate_when_unhealthy else 0.0\n",
" state.metrics.update(\n",
" forward_reward=forward_reward,\n",
" reward_linvel=forward_reward,\n",
" reward_quadctrl=-ctrl_cost,\n",
" reward_alive=healthy_reward,\n",
" x_position=com_after[0],\n",
" y_position=com_after[1],\n",
" distance_from_origin=jp.linalg.norm(com_after),\n",
" x_velocity=velocity[0],\n",
" y_velocity=velocity[1],\n",
" )\n",
"\n",
" return state.replace(\n",
" pipeline_state=data, obs=obs, reward=reward, done=done\n",
" )\n",
"\n",
" def _get_obs(\n",
" self, data: mjx.Data, action: jp.ndarray\n",
" ) -> jp.ndarray:\n",
" \"\"\"Observes humanoid body position, velocities, and angles.\"\"\"\n",
" position = data.qpos\n",
" if self._exclude_current_positions_from_observation:\n",
" position = position[2:]\n",
"\n",
" # external_contact_forces are excluded\n",
" return jp.concatenate([\n",
" position,\n",
" data.qvel,\n",
" data.cinert[1:].ravel(),\n",
" data.cvel[1:].ravel(),\n",
" data.qfrc_actuator,\n",
" ])\n",
"\n",
"\n",
"envs.register_environment('humanoid', Humanoid)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P1K6IznI2y83"
},
"source": [
"## Visualize a Rollout\n",
"\n",
"Let's instantiate the environment and visualize a short rollout.\n",
"\n",
"NOTE: Since episodes terminate early if the torso is below the healthy z-range, the only relevant contacts for this task are between the feet and the plane. We turn off other contacts."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "EhKLFK54C1CH"
},
"outputs": [],
"source": [
"# instantiate the environment\n",
"env_name = 'humanoid'\n",
"env = envs.get_environment(env_name)\n",
"\n",
"# define the jit reset/step functions\n",
"jit_reset = jax.jit(env.reset)\n",
"jit_step = jax.jit(env.step)\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "Ph8u-v2Q2xLS"
},
"outputs": [],
"source": [
"# initialize the state\n",
"state = jit_reset(jax.random.PRNGKey(0))\n",
"rollout = [state.pipeline_state]\n",
"\n",
"# grab a trajectory\n",
"for i in range(10):\n",
" ctrl = -0.1 * jp.ones(env.sys.nu)\n",
" state = jit_step(state, ctrl)\n",
" rollout.append(state.pipeline_state)\n",
"\n",
"media.show_video(env.render(rollout, camera='side'), fps=1.0 / env.dt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BQDG6NQ1CbZD"
},
"source": [
"## Train Humanoid Policy\n",
"\n",
"Let's now train a policy with PPO to make the Humanoid run forwards. Training takes about 6 minutes on a Tesla A100 GPU."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "xLiddQYPApBw"
},
"outputs": [],
"source": [
"train_fn = functools.partial(\n",
" ppo.train, num_timesteps=20_000_000, num_evals=5, reward_scaling=0.1,\n",
" episode_length=1000, normalize_observations=True, action_repeat=1,\n",
" unroll_length=10, num_minibatches=24, num_updates_per_batch=8,\n",
" discounting=0.97, learning_rate=3e-4, entropy_cost=1e-3, num_envs=3072,\n",
" batch_size=512, seed=0)\n",
"\n",
"\n",
"x_data = []\n",
"y_data = []\n",
"ydataerr = []\n",
"times = [datetime.now()]\n",
"\n",
"max_y, min_y = 13000, 0\n",
"def progress(num_steps, metrics):\n",
" times.append(datetime.now())\n",
" x_data.append(num_steps)\n",
" y_data.append(metrics['eval/episode_reward'])\n",
" ydataerr.append(metrics['eval/episode_reward_std'])\n",
"\n",
" plt.xlim([0, train_fn.keywords['num_timesteps'] * 1.25])\n",
" plt.ylim([min_y, max_y])\n",
"\n",
" plt.xlabel('# environment steps')\n",
" plt.ylabel('reward per episode')\n",
" plt.title(f'y={y_data[-1]:.3f}')\n",
"\n",
" plt.errorbar(\n",
" x_data, y_data, yerr=ydataerr)\n",
" plt.show()\n",
"\n",
"make_inference_fn, params, _= train_fn(environment=env, progress_fn=progress)\n",
"\n",
"print(f'time to jit: {times[1] - times[0]}')\n",
"print(f'time to train: {times[-1] - times[1]}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YYIch0HEApBx"
},
"source": [
"<!-- ## Save and Load Policy -->\n",
"\n",
"We can save and load the policy using the brax model API."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "Z8gI6qH6ApBx"
},
"outputs": [],
"source": [
"#@title Save Model\n",
"model_path = '/tmp/mjx_brax_policy'\n",
"model.save_params(model_path, params)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "h4reaWgxApBx"
},
"outputs": [],
"source": [
"#@title Load Model and Define Inference Function\n",
"params = model.load_params(model_path)\n",
"\n",
"inference_fn = make_inference_fn(params)\n",
"jit_inference_fn = jax.jit(inference_fn)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0G357XIfApBy"
},
"source": [
"## Visualize Policy\n",
"\n",
"Finally we can visualize the policy."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "osYasMw4ApBy"
},
"outputs": [],
"source": [
"eval_env = envs.get_environment(env_name)\n",
"\n",
"jit_reset = jax.jit(eval_env.reset)\n",
"jit_step = jax.jit(eval_env.step)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "d-UhypudApBy"
},
"outputs": [],
"source": [
"# initialize the state\n",
"rng = jax.random.PRNGKey(0)\n",
"state = jit_reset(rng)\n",
"rollout = [state.pipeline_state]\n",
"\n",
"# grab a trajectory\n",
"n_steps = 500\n",
"render_every = 2\n",
"\n",
"for i in range(n_steps):\n",
" act_rng, rng = jax.random.split(rng)\n",
" ctrl, _ = jit_inference_fn(state.obs, act_rng)\n",
" state = jit_step(state, ctrl)\n",
" rollout.append(state.pipeline_state)\n",
"\n",
" if state.done:\n",
" break\n",
"\n",
"media.show_video(env.render(rollout[::render_every], camera='side'), fps=1.0 / env.dt / render_every)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zR-heox6LARK"
},
"source": [
"# MJX Policy in MuJoCo\n",
"\n",
"We can also perform the physics step using the original MuJoCo python bindings to show that the policy trained in MJX works in MuJoCo."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "w6ixFi4dApBy"
},
"outputs": [],
"source": [
"mj_model = eval_env.sys.mj_model\n",
"mj_data = mujoco.MjData(mj_model)\n",
"\n",
"renderer = mujoco.Renderer(mj_model)\n",
"ctrl = jp.zeros(mj_model.nu)\n",
"\n",
"images = []\n",
"for i in range(n_steps):\n",
" act_rng, rng = jax.random.split(rng)\n",
"\n",
" obs = eval_env._get_obs(mjx.put_data(mj_model, mj_data), ctrl)\n",
" ctrl, _ = jit_inference_fn(obs, act_rng)\n",
"\n",
" mj_data.ctrl = ctrl\n",
" for _ in range(eval_env._n_frames):\n",
" mujoco.mj_step(mj_model, mj_data) # Physics step using MuJoCo mj_step.\n",
"\n",
" if i % render_every == 0:\n",
" renderer.update_scene(mj_data, camera='side')\n",
" images.append(renderer.render())\n",
"\n",
"media.show_video(images, fps=1.0 / eval_env.dt / render_every)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "65mIPj6DQNNa"
},
"source": [
"# Training a Policy with Domain Randomization\n",
"\n",
"We might also want to include randomization over certain `mjModel` parameters while training a policy. In MJX, we can easily create a batch of environments with randomized values populated in `mjx.Model`. Below, we show a function that randomizes friction and actuator gain/bias."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "h8mhzKjHQuoL"
},
"outputs": [],
"source": [
"def domain_randomize(sys, rng):\n",
" \"\"\"Randomizes the mjx.Model.\"\"\"\n",
" @jax.vmap\n",
" def rand(rng):\n",
" _, key = jax.random.split(rng, 2)\n",
" # friction\n",
" friction = jax.random.uniform(key, (1,), minval=0.6, maxval=1.4)\n",
" friction = sys.geom_friction.at[:, 0].set(friction)\n",
" # actuator\n",
" _, key = jax.random.split(key, 2)\n",
" gain_range = (-5, 5)\n",
" param = jax.random.uniform(\n",
" key, (1,), minval=gain_range[0], maxval=gain_range[1]\n",
" ) + sys.actuator_gainprm[:, 0]\n",
" gain = sys.actuator_gainprm.at[:, 0].set(param)\n",
" bias = sys.actuator_biasprm.at[:, 1].set(-param)\n",
" return friction, gain, bias\n",
"\n",
" friction, gain, bias = rand(rng)\n",
"\n",
" in_axes = jax.tree_util.tree_map(lambda x: None, sys)\n",
" in_axes = in_axes.tree_replace({\n",
" 'geom_friction': 0,\n",
" 'actuator_gainprm': 0,\n",
" 'actuator_biasprm': 0,\n",
" })\n",
"\n",
" sys = sys.tree_replace({\n",
" 'geom_friction': friction,\n",
" 'actuator_gainprm': gain,\n",
" 'actuator_biasprm': bias,\n",
" })\n",
"\n",
" return sys, in_axes"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gnsZo-GWSYYj"
},
"source": [
"If we wanted 10 environments with randomized friction and actuator params, we can call `domain_randomize`, which returns a batched `mjx.Model` along with a dictionary specifying the axes that are batched."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "1K45Kp2ASV9s"
},
"outputs": [],
"source": [
"rng = jax.random.PRNGKey(0)\n",
"rng = jax.random.split(rng, 10)\n",
"batched_sys, _ = domain_randomize(env.sys, rng)\n",
"\n",
"print('Single env friction shape: ', env.sys.geom_friction.shape)\n",
"print('Batched env friction shape: ', batched_sys.geom_friction.shape)\n",
"\n",
"print('Friction on geom 0: ', env.sys.geom_friction[0, 0])\n",
"print('Random frictions on geom 0: ', batched_sys.geom_friction[:, 0, 0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "efnxNOnpQFuC"
},
"source": [
"## Quadruped Env\n",
"\n",
"Let's define a quadruped environment that takes advantage of the domain randomization function. Here we use the [Barkour vb Quadruped](https://github.com/google-deepmind/mujoco_menagerie/tree/main/google_barkour_vb) from [MuJoCo Menagerie](https://github.com/google-deepmind/mujoco_menagerie). We implement an environment that trains a joystick policy with Brax.\n",
"\n",
"NOTE: for a full suite of robotic environments, many of which were transferred onto robots, check out [MuJoCo Playground](https://github.com/google-deepmind/mujoco_playground)!\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "VfyK73gtRXid"
},
"outputs": [],
"source": [
"!git clone https://github.com/google-deepmind/mujoco_menagerie\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "y79PoJOCIl-O"
},
"outputs": [],
"source": [
"#@title Barkour vb Quadruped Env\n",
"\n",
"BARKOUR_ROOT_PATH = epath.Path('mujoco_menagerie/google_barkour_vb')\n",
"\n",
"\n",
"def get_config():\n",
" \"\"\"Returns reward config for barkour quadruped environment.\"\"\"\n",
"\n",
" def get_default_rewards_config():\n",
" default_config = config_dict.ConfigDict(\n",
" dict(\n",
" # The coefficients for all reward terms used for training. All\n",
" # physical quantities are in SI units, if no otherwise specified,\n",
" # i.e. joint positions are in rad, positions are measured in meters,\n",
" # torques in Nm, and time in seconds, and forces in Newtons.\n",
" scales=config_dict.ConfigDict(\n",
" dict(\n",
" # Tracking rewards are computed using exp(-delta^2/sigma)\n",
" # sigma can be a hyperparameters to tune.\n",
" # Track the base x-y velocity (no z-velocity tracking.)\n",
" tracking_lin_vel=1.5,\n",
" # Track the angular velocity along z-axis, i.e. yaw rate.\n",
" tracking_ang_vel=0.8,\n",
" # Below are regularization terms, we roughly divide the\n",
" # terms to base state regularizations, joint\n",
" # regularizations, and other behavior regularizations.\n",
" # Penalize the base velocity in z direction, L2 penalty.\n",
" lin_vel_z=-2.0,\n",
" # Penalize the base roll and pitch rate. L2 penalty.\n",
" ang_vel_xy=-0.05,\n",
" # Penalize non-zero roll and pitch angles. L2 penalty.\n",
" orientation=-5.0,\n",
" # L2 regularization of joint torques, |tau|^2.\n",
" torques=-0.0002,\n",
" # Penalize the change in the action and encourage smooth\n",
" # actions. L2 regularization |action - last_action|^2\n",
" action_rate=-0.01,\n",
" # Encourage long swing steps. However, it does not\n",
" # encourage high clearances.\n",
" feet_air_time=0.2,\n",
" # Encourage no motion at zero command, L2 regularization\n",
" # |q - q_default|^2.\n",
" stand_still=-0.5,\n",
" # Early termination penalty.\n",
" termination=-1.0,\n",
" # Penalizing foot slipping on the ground.\n",
" foot_slip=-0.1,\n",
" )\n",
" ),\n",
" # Tracking reward = exp(-error^2/sigma).\n",
" tracking_sigma=0.25,\n",
" )\n",
" )\n",
" return default_config\n",
"\n",
" default_config = config_dict.ConfigDict(\n",
" dict(\n",
" rewards=get_default_rewards_config(),\n",
" )\n",
" )\n",
"\n",
" return default_config\n",
"\n",
"\n",
"class BarkourEnv(PipelineEnv):\n",
" \"\"\"Environment for training the barkour quadruped joystick policy in MJX.\"\"\"\n",
"\n",
" def __init__(\n",
" self,\n",
" obs_noise: float = 0.05,\n",
" action_scale: float = 0.3,\n",
" kick_vel: float = 0.05,\n",
" scene_file: str = 'scene_mjx.xml',\n",
" **kwargs,\n",
" ):\n",
" path = BARKOUR_ROOT_PATH / scene_file\n",
" sys = mjcf.load(path.as_posix())\n",
" self._dt = 0.02 # this environment is 50 fps\n",
" sys = sys.tree_replace({'opt.timestep': 0.004})\n",
"\n",
" # override menagerie params for smoother policy\n",
" sys = sys.replace(\n",
" dof_damping=sys.dof_damping.at[6:].set(0.5239),\n",
" actuator_gainprm=sys.actuator_gainprm.at[:, 0].set(35.0),\n",
" actuator_biasprm=sys.actuator_biasprm.at[:, 1].set(-35.0),\n",
" )\n",
"\n",
" n_frames = kwargs.pop('n_frames', int(self._dt / sys.opt.timestep))\n",
" super().__init__(sys, backend='mjx', n_frames=n_frames)\n",
"\n",
" self.reward_config = get_config()\n",
" # set custom from kwargs\n",
" for k, v in kwargs.items():\n",
" if k.endswith('_scale'):\n",
" self.reward_config.rewards.scales[k[:-6]] = v\n",
"\n",
" self._torso_idx = mujoco.mj_name2id(\n",
" sys.mj_model, mujoco.mjtObj.mjOBJ_BODY.value, 'torso'\n",
" )\n",
" self._action_scale = action_scale\n",
" self._obs_noise = obs_noise\n",
" self._kick_vel = kick_vel\n",
" self._init_q = jp.array(sys.mj_model.keyframe('home').qpos)\n",
" self._default_pose = sys.mj_model.keyframe('home').qpos[7:]\n",
" self.lowers = jp.array([-0.7, -1.0, 0.05] * 4)\n",
" self.uppers = jp.array([0.52, 2.1, 2.1] * 4)\n",
" feet_site = [\n",
" 'foot_front_left',\n",
" 'foot_hind_left',\n",
" 'foot_front_right',\n",
" 'foot_hind_right',\n",
" ]\n",
" feet_site_id = [\n",
" mujoco.mj_name2id(sys.mj_model, mujoco.mjtObj.mjOBJ_SITE.value, f)\n",
" for f in feet_site\n",
" ]\n",
" assert not any(id_ == -1 for id_ in feet_site_id), 'Site not found.'\n",
" self._feet_site_id = np.array(feet_site_id)\n",
" lower_leg_body = [\n",
" 'lower_leg_front_left',\n",
" 'lower_leg_hind_left',\n",
" 'lower_leg_front_right',\n",
" 'lower_leg_hind_right',\n",
" ]\n",
" lower_leg_body_id = [\n",
" mujoco.mj_name2id(sys.mj_model, mujoco.mjtObj.mjOBJ_BODY.value, l)\n",
" for l in lower_leg_body\n",
" ]\n",
" assert not any(id_ == -1 for id_ in lower_leg_body_id), 'Body not found.'\n",
" self._lower_leg_body_id = np.array(lower_leg_body_id)\n",
" self._foot_radius = 0.0175\n",
" self._nv = sys.nv\n",
"\n",
" def sample_command(self, rng: jax.Array) -> jax.Array:\n",
" lin_vel_x = [-0.6, 1.5] # min max [m/s]\n",
" lin_vel_y = [-0.8, 0.8] # min max [m/s]\n",
" ang_vel_yaw = [-0.7, 0.7] # min max [rad/s]\n",
"\n",
" _, key1, key2, key3 = jax.random.split(rng, 4)\n",
" lin_vel_x = jax.random.uniform(\n",
" key1, (1,), minval=lin_vel_x[0], maxval=lin_vel_x[1]\n",
" )\n",
" lin_vel_y = jax.random.uniform(\n",
" key2, (1,), minval=lin_vel_y[0], maxval=lin_vel_y[1]\n",
" )\n",
" ang_vel_yaw = jax.random.uniform(\n",
" key3, (1,), minval=ang_vel_yaw[0], maxval=ang_vel_yaw[1]\n",
" )\n",
" new_cmd = jp.array([lin_vel_x[0], lin_vel_y[0], ang_vel_yaw[0]])\n",
" return new_cmd\n",
"\n",
" def reset(self, rng: jax.Array) -> State: # pytype: disable=signature-mismatch\n",
" rng, key = jax.random.split(rng)\n",
"\n",
" pipeline_state = self.pipeline_init(self._init_q, jp.zeros(self._nv))\n",
"\n",
" state_info = {\n",
" 'rng': rng,\n",
" 'last_act': jp.zeros(12),\n",
" 'last_vel': jp.zeros(12),\n",
" 'command': self.sample_command(key),\n",
" 'last_contact': jp.zeros(4, dtype=bool),\n",
" 'feet_air_time': jp.zeros(4),\n",
" 'rewards': {k: 0.0 for k in self.reward_config.rewards.scales.keys()},\n",
" 'kick': jp.array([0.0, 0.0]),\n",
" 'step': 0,\n",
" }\n",
"\n",
" obs_history = jp.zeros(15 * 31) # store 15 steps of history\n",
" obs = self._get_obs(pipeline_state, state_info, obs_history)\n",
" reward, done = jp.zeros(2)\n",
" metrics = {'total_dist': 0.0}\n",
" for k in state_info['rewards']:\n",
" metrics[k] = state_info['rewards'][k]\n",
" state = State(pipeline_state, obs, reward, done, metrics, state_info) # pytype: disable=wrong-arg-types\n",
" return state\n",
"\n",
" def step(self, state: State, action: jax.Array) -> State: # pytype: disable=signature-mismatch\n",
" rng, cmd_rng, kick_noise_2 = jax.random.split(state.info['rng'], 3)\n",
"\n",
" # kick\n",
" push_interval = 10\n",
" kick_theta = jax.random.uniform(kick_noise_2, maxval=2 * jp.pi)\n",
" kick = jp.array([jp.cos(kick_theta), jp.sin(kick_theta)])\n",
" kick *= jp.mod(state.info['step'], push_interval) == 0\n",
" qvel = state.pipeline_state.qvel # pytype: disable=attribute-error\n",
" qvel = qvel.at[:2].set(kick * self._kick_vel + qvel[:2])\n",
" state = state.tree_replace({'pipeline_state.qvel': qvel})\n",
"\n",
" # physics step\n",
" motor_targets = self._default_pose + action * self._action_scale\n",
" motor_targets = jp.clip(motor_targets, self.lowers, self.uppers)\n",
" pipeline_state = self.pipeline_step(state.pipeline_state, motor_targets)\n",
" x, xd = pipeline_state.x, pipeline_state.xd\n",
"\n",
" # observation data\n",
" obs = self._get_obs(pipeline_state, state.info, state.obs)\n",
" joint_angles = pipeline_state.q[7:]\n",
" joint_vel = pipeline_state.qd[6:]\n",
"\n",
" # foot contact data based on z-position\n",
" foot_pos = pipeline_state.site_xpos[self._feet_site_id] # pytype: disable=attribute-error\n",
" foot_contact_z = foot_pos[:, 2] - self._foot_radius\n",
" contact = foot_contact_z < 1e-3 # a mm or less off the floor\n",
" contact_filt_mm = contact | state.info['last_contact']\n",
" contact_filt_cm = (foot_contact_z < 3e-2) | state.info['last_contact']\n",
" first_contact = (state.info['feet_air_time'] > 0) * contact_filt_mm\n",
" state.info['feet_air_time'] += self.dt\n",
"\n",
" # done if joint limits are reached or robot is falling\n",
" up = jp.array([0.0, 0.0, 1.0])\n",
" done = jp.dot(math.rotate(up, x.rot[self._torso_idx - 1]), up) < 0\n",
" done |= jp.any(joint_angles < self.lowers)\n",
" done |= jp.any(joint_angles > self.uppers)\n",
" done |= pipeline_state.x.pos[self._torso_idx - 1, 2] < 0.18\n",
"\n",
" # reward\n",
" rewards = {\n",
" 'tracking_lin_vel': (\n",
" self._reward_tracking_lin_vel(state.info['command'], x, xd)\n",
" ),\n",
" 'tracking_ang_vel': (\n",
" self._reward_tracking_ang_vel(state.info['command'], x, xd)\n",
" ),\n",
" 'lin_vel_z': self._reward_lin_vel_z(xd),\n",
" 'ang_vel_xy': self._reward_ang_vel_xy(xd),\n",
" 'orientation': self._reward_orientation(x),\n",
" 'torques': self._reward_torques(pipeline_state.qfrc_actuator), # pytype: disable=attribute-error\n",
" 'action_rate': self._reward_action_rate(action, state.info['last_act']),\n",
" 'stand_still': self._reward_stand_still(\n",
" state.info['command'], joint_angles,\n",
" ),\n",
" 'feet_air_time': self._reward_feet_air_time(\n",
" state.info['feet_air_time'],\n",
" first_contact,\n",
" state.info['command'],\n",
" ),\n",
" 'foot_slip': self._reward_foot_slip(pipeline_state, contact_filt_cm),\n",
" 'termination': self._reward_termination(done, state.info['step']),\n",
" }\n",
" rewards = {\n",
" k: v * self.reward_config.rewards.scales[k] for k, v in rewards.items()\n",
" }\n",
" reward = jp.clip(sum(rewards.values()) * self.dt, 0.0, 10000.0)\n",
"\n",
" # state management\n",
" state.info['kick'] = kick\n",
" state.info['last_act'] = action\n",
" state.info['last_vel'] = joint_vel\n",
" state.info['feet_air_time'] *= ~contact_filt_mm\n",
" state.info['last_contact'] = contact\n",
" state.info['rewards'] = rewards\n",
" state.info['step'] += 1\n",
" state.info['rng'] = rng\n",
"\n",
" # sample new command if more than 500 timesteps achieved\n",
" state.info['command'] = jp.where(\n",
" state.info['step'] > 500,\n",
" self.sample_command(cmd_rng),\n",
" state.info['command'],\n",
" )\n",
" # reset the step counter when done\n",
" state.info['step'] = jp.where(\n",
" done | (state.info['step'] > 500), 0, state.info['step']\n",
" )\n",
"\n",
" # log total displacement as a proxy metric\n",
" state.metrics['total_dist'] = math.normalize(x.pos[self._torso_idx - 1])[1]\n",
" state.metrics.update(state.info['rewards'])\n",
"\n",
" done = jp.float32(done)\n",
" state = state.replace(\n",
" pipeline_state=pipeline_state, obs=obs, reward=reward, done=done\n",
" )\n",
" return state\n",
"\n",
" def _get_obs(\n",
" self,\n",
" pipeline_state: base.State,\n",
" state_info: dict[str, Any],\n",
" obs_history: jax.Array,\n",
" ) -> jax.Array:\n",
" inv_torso_rot = math.quat_inv(pipeline_state.x.rot[0])\n",
" local_rpyrate = math.rotate(pipeline_state.xd.ang[0], inv_torso_rot)\n",
"\n",
" obs = jp.concatenate([\n",
" jp.array([local_rpyrate[2]]) * 0.25, # yaw rate\n",
" math.rotate(jp.array([0, 0, -1]), inv_torso_rot), # projected gravity\n",
" state_info['command'] * jp.array([2.0, 2.0, 0.25]), # command\n",
" pipeline_state.q[7:] - self._default_pose, # motor angles\n",
" state_info['last_act'], # last action\n",
" ])\n",
"\n",
" # clip, noise\n",
" obs = jp.clip(obs, -100.0, 100.0) + self._obs_noise * jax.random.uniform(\n",
" state_info['rng'], obs.shape, minval=-1, maxval=1\n",
" )\n",
" # stack observations through time\n",
" obs = jp.roll(obs_history, obs.size).at[:obs.size].set(obs)\n",
"\n",
" return obs\n",
"\n",
" # ------------ reward functions----------------\n",
" def _reward_lin_vel_z(self, xd: Motion) -> jax.Array:\n",
" # Penalize z axis base linear velocity\n",
" return jp.square(xd.vel[0, 2])\n",
"\n",
" def _reward_ang_vel_xy(self, xd: Motion) -> jax.Array:\n",
" # Penalize xy axes base angular velocity\n",
" return jp.sum(jp.square(xd.ang[0, :2]))\n",
"\n",
" def _reward_orientation(self, x: Transform) -> jax.Array:\n",
" # Penalize non flat base orientation\n",
" up = jp.array([0.0, 0.0, 1.0])\n",
" rot_up = math.rotate(up, x.rot[0])\n",
" return jp.sum(jp.square(rot_up[:2]))\n",
"\n",
" def _reward_torques(self, torques: jax.Array) -> jax.Array:\n",
" # Penalize torques\n",
" return jp.sqrt(jp.sum(jp.square(torques))) + jp.sum(jp.abs(torques))\n",
"\n",
" def _reward_action_rate(\n",
" self, act: jax.Array, last_act: jax.Array\n",
" ) -> jax.Array:\n",
" # Penalize changes in actions\n",
" return jp.sum(jp.square(act - last_act))\n",
"\n",
" def _reward_tracking_lin_vel(\n",
" self, commands: jax.Array, x: Transform, xd: Motion\n",
" ) -> jax.Array:\n",
" # Tracking of linear velocity commands (xy axes)\n",
" local_vel = math.rotate(xd.vel[0], math.quat_inv(x.rot[0]))\n",
" lin_vel_error = jp.sum(jp.square(commands[:2] - local_vel[:2]))\n",
" lin_vel_reward = jp.exp(\n",
" -lin_vel_error / self.reward_config.rewards.tracking_sigma\n",
" )\n",
" return lin_vel_reward\n",
"\n",
" def _reward_tracking_ang_vel(\n",
" self, commands: jax.Array, x: Transform, xd: Motion\n",
" ) -> jax.Array:\n",
" # Tracking of angular velocity commands (yaw)\n",
" base_ang_vel = math.rotate(xd.ang[0], math.quat_inv(x.rot[0]))\n",
" ang_vel_error = jp.square(commands[2] - base_ang_vel[2])\n",
" return jp.exp(-ang_vel_error / self.reward_config.rewards.tracking_sigma)\n",
"\n",
" def _reward_feet_air_time(\n",
" self, air_time: jax.Array, first_contact: jax.Array, commands: jax.Array\n",
" ) -> jax.Array:\n",
" # Reward air time.\n",
" rew_air_time = jp.sum((air_time - 0.1) * first_contact)\n",
" rew_air_time *= (\n",
" math.normalize(commands[:2])[1] > 0.05\n",
" ) # no reward for zero command\n",
" return rew_air_time\n",
"\n",
" def _reward_stand_still(\n",
" self,\n",
" commands: jax.Array,\n",
" joint_angles: jax.Array,\n",
" ) -> jax.Array:\n",
" # Penalize motion at zero commands\n",
" return jp.sum(jp.abs(joint_angles - self._default_pose)) * (\n",
" math.normalize(commands[:2])[1] < 0.1\n",
" )\n",
"\n",
" def _reward_foot_slip(\n",
" self, pipeline_state: base.State, contact_filt: jax.Array\n",
" ) -> jax.Array:\n",
" # get velocities at feet which are offset from lower legs\n",
" # pytype: disable=attribute-error\n",
" pos = pipeline_state.site_xpos[self._feet_site_id] # feet position\n",
" feet_offset = pos - pipeline_state.xpos[self._lower_leg_body_id]\n",
" # pytype: enable=attribute-error\n",
" offset = base.Transform.create(pos=feet_offset)\n",
" foot_indices = self._lower_leg_body_id - 1 # we got rid of the world body\n",
" foot_vel = offset.vmap().do(pipeline_state.xd.take(foot_indices)).vel\n",
"\n",
" # Penalize large feet velocity for feet that are in contact with the ground.\n",
" return jp.sum(jp.square(foot_vel[:, :2]) * contact_filt.reshape((-1, 1)))\n",
"\n",
" def _reward_termination(self, done: jax.Array, step: jax.Array) -> jax.Array:\n",
" return done & (step < 500)\n",
"\n",
" def render(\n",
" self, trajectory: List[base.State], camera: str | None = None,\n",
" width: int = 240, height: int = 320,\n",
" ) -> Sequence[np.ndarray]:\n",
" camera = camera or 'track'\n",
" return super().render(trajectory, camera=camera, width=width, height=height)\n",
"\n",
"envs.register_environment('barkour', BarkourEnv)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "pi_yrcz-Qp3W"
},
"outputs": [],
"source": [
"env_name = 'barkour'\n",
"env = envs.get_environment(env_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nxaNFP9mA23H"
},
"source": [
"## Train Policy\n",
"\n",
"To train a policy with domain randomization, we pass in the domain randomization function into the brax train function; brax will call the domain randomization function when rolling out episodes. Training the quadruped takes 6 minutes on a Tesla A100 GPU."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "cHJCbESGA7Rk"
},
"outputs": [],
"source": [
"ckpt_path = epath.Path('/tmp/quadrupred_joystick/ckpts')\n",
"ckpt_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
"def policy_params_fn(current_step, make_policy, params):\n",
" # save checkpoints\n",
" orbax_checkpointer = ocp.PyTreeCheckpointer()\n",
" save_args = orbax_utils.save_args_from_target(params)\n",
" path = ckpt_path / f'{current_step}'\n",
" orbax_checkpointer.save(path, params, force=True, save_args=save_args)\n",
"\n",
"\n",
"make_networks_factory = functools.partial(\n",
" ppo_networks.make_ppo_networks,\n",
" policy_hidden_layer_sizes=(128, 128, 128, 128))\n",
"train_fn = functools.partial(\n",
" ppo.train, num_timesteps=100_000_000, num_evals=10,\n",
" reward_scaling=1, episode_length=1000, normalize_observations=True,\n",
" action_repeat=1, unroll_length=20, num_minibatches=32,\n",
" num_updates_per_batch=4, discounting=0.97, learning_rate=3.0e-4,\n",
" entropy_cost=1e-2, num_envs=8192, batch_size=256,\n",
" network_factory=make_networks_factory,\n",
" randomization_fn=domain_randomize,\n",
" policy_params_fn=policy_params_fn,\n",
" seed=0)\n",
"\n",
"x_data = []\n",
"y_data = []\n",
"ydataerr = []\n",
"times = [datetime.now()]\n",
"max_y, min_y = 40, 0\n",
"\n",
"# Reset environments since internals may be overwritten by tracers from the\n",
"# domain randomization function.\n",
"env = envs.get_environment(env_name)\n",
"eval_env = envs.get_environment(env_name)\n",
"make_inference_fn, params, _= train_fn(environment=env,\n",
" progress_fn=progress,\n",
" eval_env=eval_env)\n",
"\n",
"print(f'time to jit: {times[1] - times[0]}')\n",
"print(f'time to train: {times[-1] - times[1]}')"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "8Yge-CGP5JoO"
},
"outputs": [],
"source": [
"# Save and reload params.\n",
"model_path = '/tmp/mjx_brax_quadruped_policy'\n",
"model.save_params(model_path, params)\n",
"params = model.load_params(model_path)\n",
"\n",
"inference_fn = make_inference_fn(params)\n",
"jit_inference_fn = jax.jit(inference_fn)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L01IrN4oCIkC"
},
"source": [
"## Visualize Policy\n",
"\n",
"For the Barkour Quadruped, the joystick commands can be set through `x_vel`, `y_vel`, and `ang_vel`. `x_vel` and `y_vel` define the linear forward and sideways velocities with respect to the quadruped torso. `ang_vel` defines the angular velocity of the torso in the z direction."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "VTbpEtXnEecd"
},
"outputs": [],
"source": [
"eval_env = envs.get_environment(env_name)\n",
"\n",
"jit_reset = jax.jit(eval_env.reset)\n",
"jit_step = jax.jit(eval_env.step)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "HRRN-8L-BivZ"
},
"outputs": [],
"source": [
"# @markdown Commands **only used for Barkour Env**:\n",
"x_vel = 1.0 #@param {type: \"number\"}\n",
"y_vel = 0.0 #@param {type: \"number\"}\n",
"ang_vel = -0.5 #@param {type: \"number\"}\n",
"\n",
"the_command = jp.array([x_vel, y_vel, ang_vel])\n",
"\n",
"# initialize the state\n",
"rng = jax.random.PRNGKey(0)\n",
"state = jit_reset(rng)\n",
"state.info['command'] = the_command\n",
"rollout = [state.pipeline_state]\n",
"\n",
"# grab a trajectory\n",
"n_steps = 500\n",
"render_every = 2\n",
"\n",
"for i in range(n_steps):\n",
" act_rng, rng = jax.random.split(rng)\n",
" ctrl, _ = jit_inference_fn(state.obs, act_rng)\n",
" state = jit_step(state, ctrl)\n",
" rollout.append(state.pipeline_state)\n",
"\n",
"media.show_video(\n",
" eval_env.render(rollout[::render_every], camera='track'),\n",
" fps=1.0 / eval_env.dt / render_every)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aD6H6WD0915X"
},
"source": [
"We can also render the rollout using the Brax renderer."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "V7jqv08X95u4"
},
"outputs": [],
"source": [
"HTML(html.render(eval_env.sys.tree_replace({'opt.timestep': eval_env.dt}), rollout))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gNagGnBODotY"
},
"source": [
"## Train Policy with Height Field\n",
"\n",
"We may also want the quadruped to learn to walk on rought terrain. Let's take the latest checkpoint from the joystick policy above, and finetune it on a height field terrain."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "wlT3xLouKxqT"
},
"outputs": [],
"source": [
"# use the height field scene\n",
"scene_file = 'scene_hfield_mjx.xml'\n",
"\n",
"env = envs.get_environment(env_name, scene_file=scene_file)\n",
"jit_reset = jax.jit(env.reset)\n",
"state = jit_reset(jax.random.PRNGKey(0))\n",
"plt.imshow(env.render([state.pipeline_state], camera='track')[0])"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "jOTx-OyPDqPW"
},
"outputs": [],
"source": [
"# grab the latest checkpoint from the flat terrain joystick policy\n",
"latest_ckpts = list(ckpt_path.glob('*'))\n",
"latest_ckpts.sort(key=lambda x: int(x.as_posix().split('/')[-1]))\n",
"latest_ckpt = latest_ckpts[-1]\n",
"\n",
"train_fn = functools.partial(\n",
" ppo.train, num_timesteps=40_000_000, num_evals=5,\n",
" reward_scaling=1, episode_length=1000, normalize_observations=True,\n",
" action_repeat=1, unroll_length=20, num_minibatches=32,\n",
" num_updates_per_batch=4, discounting=0.97, learning_rate=3.0e-4,\n",
" entropy_cost=1e-2, num_envs=8192, batch_size=256,\n",
" network_factory=make_networks_factory,\n",
" randomization_fn=domain_randomize, seed=0,\n",
" restore_checkpoint_path=latest_ckpt)\n",
"\n",
"x_data = []\n",
"y_data = []\n",
"ydataerr = []\n",
"times = [datetime.now()]\n",
"max_y, min_y = 40, 0\n",
"\n",
"# Reset environments since internals may be overwritten by tracers from the\n",
"# domain randomization function.\n",
"env = envs.get_environment(env_name, scene_file=scene_file)\n",
"eval_env = envs.get_environment(env_name, scene_file=scene_file)\n",
"make_inference_fn, params, _= train_fn(environment=env,\n",
" progress_fn=progress,\n",
" eval_env=eval_env)\n",
"\n",
"print(f'time to jit: {times[1] - times[0]}')\n",
"print(f'time to train: {times[-1] - times[1]}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0wHRJyjLFww6"
},
"source": [
"## Visualize Policy with Height Field"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "1X57XkaVFu-v"
},
"outputs": [],
"source": [
"eval_env = envs.get_environment(env_name, scene_file=scene_file)\n",
"\n",
"jit_reset = jax.jit(eval_env.reset)\n",
"jit_step = jax.jit(eval_env.step)\n",
"inference_fn = make_inference_fn(params)\n",
"jit_inference_fn = jax.jit(inference_fn)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"id": "nAxexZcVFu-v"
},
"outputs": [],
"source": [
"# @markdown Commands **only used for Barkour Env**:\n",
"x_vel = 1.0 #@param {type: \"number\"}\n",
"y_vel = 0.0 #@param {type: \"number\"}\n",
"ang_vel = -0.5 #@param {type: \"number\"}\n",
"\n",
"the_command = jp.array([x_vel, y_vel, ang_vel])\n",
"\n",
"# initialize the state\n",
"rng = jax.random.PRNGKey(0)\n",
"state = jit_reset(rng)\n",
"state.info['command'] = the_command\n",
"rollout = [state.pipeline_state]\n",
"\n",
"# grab a trajectory\n",
"n_steps = 500\n",
"render_every = 2\n",
"\n",
"for i in range(n_steps):\n",
" act_rng, rng = jax.random.split(rng)\n",
" ctrl, _ = jit_inference_fn(state.obs, act_rng)\n",
" state = jit_step(state, ctrl)\n",
" rollout.append(state.pipeline_state)\n",
"\n",
"media.show_video(\n",
" eval_env.render(rollout[::render_every], camera='track'),\n",
" fps=1.0 / eval_env.dt / render_every)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-Q5gsOOaBYd1"
},
"source": [
"# MuJoCo Playground: Robotics locomotion and manipulation environments + Sim-to-Real!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gluTlHURuC6i"
},
"source": [
"By now, we have shown how MJX can be used to train policies for classic control and robotic locomotion. For a full suite of robotic locomotion and manipulation environments we encourage you to check out [MuJoCo Playground](https://github.com/google-deepmind/mujoco_playground). Many of the robotic environments have been transferred onto robots, as described on the [website](https://playground.mujoco.org/) and [technical report](https://playground.mujoco.org/assets/playground_technical_report.pdf).\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuClass": "premium",
"gpuType": "V100",
"machine_shape": "hm",
"private_outputs": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}