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<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Load custom quadruped robot environments</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="load-custom-quadruped-robot-environments">
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<h1>Load custom quadruped robot environments<a class="headerlink" href="#load-custom-quadruped-robot-environments" title="Link to this heading">¶</a></h1>
|
||
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<p>In this tutorial we will see how to use the <code class="docutils literal notranslate"><span class="pre">MuJoCo/Ant-v5</span></code> framework to create a quadruped walking environment, using a model file (ending in <code class="docutils literal notranslate"><span class="pre">.xml</span></code>) without having to create a new class.</p>
|
||
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<p>Steps:</p>
|
||
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<ol class="arabic simple" start="0">
|
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<li><p>Get your <strong>MJCF</strong> (or <strong>URDF</strong>) model file of your robot.</p>
|
||
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<ul class="simple">
|
||
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<li><p>Create your own model (see the <a class="reference external" href="https://mujoco.readthedocs.io/en/stable/m22odeling.html">Guide</a>) or,</p></li>
|
||
|
|
<li><p>Find a ready-made model (in this tutorial, we will use a model from the <a class="reference external" href="https://github.com/google-deepmind/mujoco_menagerie"><strong>MuJoCo Menagerie</strong></a> collection).</p></li>
|
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</ul>
|
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</li>
|
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<li><p>Load the model with the <code class="docutils literal notranslate"><span class="pre">xml_file</span></code> argument.</p></li>
|
||
|
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<li><p>Tweak the environment parameters to get the desired behavior.</p>
|
||
|
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<ol class="arabic simple">
|
||
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<li><p>Tweak the environment simulation parameters.</p></li>
|
||
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<li><p>Tweak the environment termination parameters.</p></li>
|
||
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<li><p>Tweak the environment reward parameters.</p></li>
|
||
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<li><p>Tweak the environment observation parameters.</p></li>
|
||
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</ol>
|
||
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</li>
|
||
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<li><p>Train an agent to move your robot.</p></li>
|
||
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</ol>
|
||
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<p>The reader is expected to be familiar with the <code class="docutils literal notranslate"><span class="pre">Gymnasium</span></code> API & library, the basics of robotics, and the included <code class="docutils literal notranslate"><span class="pre">Gymnasium/MuJoCo</span></code> environments with the robot model they use. Familiarity with the <strong>MJCF</strong> file model format and the <code class="docutils literal notranslate"><span class="pre">MuJoCo</span></code> simulator is not required but is recommended.</p>
|
||
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<section id="setup">
|
||
|
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<h2>Setup<a class="headerlink" href="#setup" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>We will need <code class="docutils literal notranslate"><span class="pre">gymnasium>=1.0.0</span></code>.</p>
|
||
|
|
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">"gymnasium>=1.0.0"</span>
|
||
|
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</pre></div>
|
||
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</div>
|
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</section>
|
||
|
|
<section id="step-0-1-download-a-robot-model">
|
||
|
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<h2>Step 0.1 - Download a Robot Model<a class="headerlink" href="#step-0-1-download-a-robot-model" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>In this tutorial we will load the <a class="reference external" href="https://github.com/google-deepmind/mujoco_menagerie/blob/main/unitree_go1/README.md">Unitree Go1</a> robot from the excellent <a class="reference external" href="https://github.com/google-deepmind/mujoco_menagerie">MuJoCo Menagerie</a> robot model collection.
|
||
|
|
<img alt="Unitree Go1 robot in a flat terrain scene" src="https://github.com/google-deepmind/mujoco_menagerie/blob/main/unitree_go1/go1.png?raw=true" /></p>
|
||
|
|
<p><code class="docutils literal notranslate"><span class="pre">Go1</span></code> is a quadruped robot, controlling it to move is a significant learning problem, much harder than the <code class="docutils literal notranslate"><span class="pre">Gymnasium/MuJoCo/Ant</span></code> environment.</p>
|
||
|
|
<p>We can download the whole MuJoCo Menagerie collection (which includes <code class="docutils literal notranslate"><span class="pre">Go1</span></code>),</p>
|
||
|
|
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/google-deepmind/mujoco_menagerie.git
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
<p>You can use any other quadruped robot with this tutorial, just adjust the environment parameter values for your robot.</p>
|
||
|
|
</section>
|
||
|
|
<section id="step-1-load-the-model">
|
||
|
|
<h2>Step 1 - Load the model<a class="headerlink" href="#step-1-load-the-model" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>To load the model, all we have to do is use the <code class="docutils literal notranslate"><span class="pre">xml_file</span></code> argument with the <code class="docutils literal notranslate"><span class="pre">Ant-v5</span></code> framework.</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">gymnasium</span>
|
||
|
|
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||
|
|
<span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">'Ant-v5'</span><span class="p">,</span> <span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">)</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
<p>Although this is enough to load the model, we will need to tweak some environment parameters to get the desired behavior for our environment, for now we will also explicitly set the simulation, termination, reward and observation arguments, which we will tweak in the next step.</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span>
|
||
|
|
<span class="s1">'Ant-v5'</span><span class="p">,</span>
|
||
|
|
<span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">,</span>
|
||
|
|
<span class="n">forward_reward_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">ctrl_cost_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">contact_cost_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_reward</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">main_body</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_z_range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">),</span>
|
||
|
|
<span class="n">include_cfrc_ext_in_observation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
|
|
<span class="n">exclude_current_positions_from_observation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||
|
|
<span class="n">reset_noise_scale</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">frame_skip</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
|
||
|
|
<span class="p">)</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
</section>
|
||
|
|
<section id="step-2-tweaking-the-environment-parameters">
|
||
|
|
<h2>Step 2 - Tweaking the Environment Parameters<a class="headerlink" href="#step-2-tweaking-the-environment-parameters" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>Tweaking the environment parameters is essential to get the desired behavior for learning.
|
||
|
|
In the following subsections, the reader is encouraged to consult the <a class="reference external" href="https://gymnasium.farama.org/main/environments/mujoco/ant/#arguments">documentation of the arguments</a> for more detailed information.</p>
|
||
|
|
</section>
|
||
|
|
<section id="step-2-1-tweaking-the-environment-simulation-parameters">
|
||
|
|
<h2>Step 2.1 - Tweaking the Environment Simulation Parameters<a class="headerlink" href="#step-2-1-tweaking-the-environment-simulation-parameters" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>The arguments of interest are <code class="docutils literal notranslate"><span class="pre">frame_skip</span></code>, <code class="docutils literal notranslate"><span class="pre">reset_noise_scale</span></code> and <code class="docutils literal notranslate"><span class="pre">max_episode_steps</span></code>.</p>
|
||
|
|
<p>We want to tweak the <code class="docutils literal notranslate"><span class="pre">frame_skip</span></code> parameter to get <code class="docutils literal notranslate"><span class="pre">dt</span></code> to an acceptable value (typical values are <code class="docutils literal notranslate"><span class="pre">dt</span></code> <span class="math notranslate nohighlight">\(\in [0.01, 0.1]\)</span> seconds),</p>
|
||
|
|
<p>Reminder: <span class="math notranslate nohighlight">\(dt = frame\_skip \times model.opt.timestep\)</span>, where <code class="docutils literal notranslate"><span class="pre">model.opt.timestep</span></code> is the integrator time step selected in the MJCF model file.</p>
|
||
|
|
<p>The <code class="docutils literal notranslate"><span class="pre">Go1</span></code> model we are using has an integrator timestep of <code class="docutils literal notranslate"><span class="pre">0.002</span></code>, so by selecting <code class="docutils literal notranslate"><span class="pre">frame_skip=25</span></code> we can set the value of <code class="docutils literal notranslate"><span class="pre">dt</span></code> to <code class="docutils literal notranslate"><span class="pre">0.05s</span></code>.</p>
|
||
|
|
<p>To avoid overfitting the policy, <code class="docutils literal notranslate"><span class="pre">reset_noise_scale</span></code> should be set to a value appropriate to the size of the robot, we want the value to be as large as possible without the initial distribution of states being invalid (<code class="docutils literal notranslate"><span class="pre">Terminal</span></code> regardless of control actions), for <code class="docutils literal notranslate"><span class="pre">Go1</span></code> we choose a value of <code class="docutils literal notranslate"><span class="pre">0.1</span></code>.</p>
|
||
|
|
<p>And <code class="docutils literal notranslate"><span class="pre">max_episode_steps</span></code> determines the number of steps per episode before <code class="docutils literal notranslate"><span class="pre">truncation</span></code>, here we set it to 1000 to be consistent with the based <code class="docutils literal notranslate"><span class="pre">Gymnasium/MuJoCo</span></code> environments, but if you need something higher you can set it so.</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span>
|
||
|
|
<span class="s1">'Ant-v5'</span><span class="p">,</span>
|
||
|
|
<span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">,</span>
|
||
|
|
<span class="n">forward_reward_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">ctrl_cost_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">contact_cost_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_reward</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">main_body</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_z_range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">),</span>
|
||
|
|
<span class="n">include_cfrc_ext_in_observation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
|
|
<span class="n">exclude_current_positions_from_observation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||
|
|
<span class="n">reset_noise_scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="c1"># set to avoid policy overfitting</span>
|
||
|
|
<span class="n">frame_skip</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="c1"># set dt=0.05</span>
|
||
|
|
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="c1"># kept at 1000</span>
|
||
|
|
<span class="p">)</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
</section>
|
||
|
|
<section id="step-2-2-tweaking-the-environment-termination-parameters">
|
||
|
|
<h2>Step 2.2 - Tweaking the Environment Termination Parameters<a class="headerlink" href="#step-2-2-tweaking-the-environment-termination-parameters" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>Termination is important for robot environments to avoid sampling “useless” time steps.</p>
|
||
|
|
<p>The arguments of interest are <code class="docutils literal notranslate"><span class="pre">terminate_when_unhealthy</span></code> and <code class="docutils literal notranslate"><span class="pre">healthy_z_range</span></code>.</p>
|
||
|
|
<p>We want to set <code class="docutils literal notranslate"><span class="pre">healthy_z_range</span></code> to terminate the environment when the robot falls over, or jumps really high, here we have to choose a value that is logical for the height of the robot, for <code class="docutils literal notranslate"><span class="pre">Go1</span></code> we choose <code class="docutils literal notranslate"><span class="pre">(0.195,</span> <span class="pre">0.75)</span></code>.
|
||
|
|
Note: <code class="docutils literal notranslate"><span class="pre">healthy_z_range</span></code> checks the absolute value of the height of the robot, so if your scene contains different levels of elevation it should be set to <code class="docutils literal notranslate"><span class="pre">(-np.inf,</span> <span class="pre">np.inf)</span></code></p>
|
||
|
|
<p>We could also set <code class="docutils literal notranslate"><span class="pre">terminate_when_unhealthy=False</span></code> to disable termination altogether, which is not desirable in the case of <code class="docutils literal notranslate"><span class="pre">Go1</span></code>.</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span>
|
||
|
|
<span class="s1">'Ant-v5'</span><span class="p">,</span>
|
||
|
|
<span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">,</span>
|
||
|
|
<span class="n">forward_reward_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">ctrl_cost_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">contact_cost_weight</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_reward</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||
|
|
<span class="n">main_body</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_z_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.195</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">),</span> <span class="c1"># set to avoid sampling steps where the robot has fallen or jumped too high</span>
|
||
|
|
<span class="n">include_cfrc_ext_in_observation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
|
|
<span class="n">exclude_current_positions_from_observation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||
|
|
<span class="n">reset_noise_scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
|
||
|
|
<span class="n">frame_skip</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
|
||
|
|
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
|
||
|
|
<span class="p">)</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
<p>Note: If you need a different termination condition, you can write your own <code class="docutils literal notranslate"><span class="pre">TerminationWrapper</span></code> (see the <a class="reference external" href="https://gymnasium.farama.org/main/api/wrappers/">documentation</a>).</p>
|
||
|
|
</section>
|
||
|
|
<section id="step-2-3-tweaking-the-environment-reward-parameters">
|
||
|
|
<h2>Step 2.3 - Tweaking the Environment Reward Parameters<a class="headerlink" href="#step-2-3-tweaking-the-environment-reward-parameters" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>The arguments of interest are <code class="docutils literal notranslate"><span class="pre">forward_reward_weight</span></code>, <code class="docutils literal notranslate"><span class="pre">ctrl_cost_weight</span></code>, <code class="docutils literal notranslate"><span class="pre">contact_cost_weight</span></code>, <code class="docutils literal notranslate"><span class="pre">healthy_reward</span></code>, and <code class="docutils literal notranslate"><span class="pre">main_body</span></code>.</p>
|
||
|
|
<p>For the arguments <code class="docutils literal notranslate"><span class="pre">forward_reward_weight</span></code>, <code class="docutils literal notranslate"><span class="pre">ctrl_cost_weight</span></code>, <code class="docutils literal notranslate"><span class="pre">contact_cost_weight</span></code> and <code class="docutils literal notranslate"><span class="pre">healthy_reward</span></code> we have to pick values that make sense for our robot, you can use the default <code class="docutils literal notranslate"><span class="pre">MuJoCo/Ant</span></code> parameters for references and tweak them if a change is needed for your environment. In the case of <code class="docutils literal notranslate"><span class="pre">Go1</span></code> we only change the <code class="docutils literal notranslate"><span class="pre">ctrl_cost_weight</span></code> since it has a higher actuator force range.</p>
|
||
|
|
<p>For the argument <code class="docutils literal notranslate"><span class="pre">main_body</span></code> we have to choose which body part is the main body (usually called something like “torso” or “trunk” in the model file) for the calculation of the <code class="docutils literal notranslate"><span class="pre">forward_reward</span></code>, in the case of <code class="docutils literal notranslate"><span class="pre">Go1</span></code> it is the <code class="docutils literal notranslate"><span class="pre">"trunk"</span></code> (Note: in most cases including this one, it can be left at the default value).</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span>
|
||
|
|
<span class="s1">'Ant-v5'</span><span class="p">,</span>
|
||
|
|
<span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">,</span>
|
||
|
|
<span class="n">forward_reward_weight</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="c1"># kept the same as the 'Ant' environment</span>
|
||
|
|
<span class="n">ctrl_cost_weight</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="c1"># changed because of the stronger motors of `Go1`</span>
|
||
|
|
<span class="n">contact_cost_weight</span><span class="o">=</span><span class="mf">5e-4</span><span class="p">,</span> <span class="c1"># kept the same as the 'Ant' environment</span>
|
||
|
|
<span class="n">healthy_reward</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="c1"># kept the same as the 'Ant' environment</span>
|
||
|
|
<span class="n">main_body</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="c1"># represents the "trunk" of the `Go1` robot</span>
|
||
|
|
<span class="n">healthy_z_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.195</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">),</span>
|
||
|
|
<span class="n">include_cfrc_ext_in_observation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
|
|
<span class="n">exclude_current_positions_from_observation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||
|
|
<span class="n">reset_noise_scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
|
||
|
|
<span class="n">frame_skip</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
|
||
|
|
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
|
||
|
|
<span class="p">)</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
<p>Note: If you need a different reward function, you can write your own <code class="docutils literal notranslate"><span class="pre">RewardWrapper</span></code> (see the <a class="reference external" href="https://gymnasium.farama.org/main/api/wrappers/reward_wrappers/">documentation</a>).</p>
|
||
|
|
</section>
|
||
|
|
<section id="step-2-4-tweaking-the-environment-observation-parameters">
|
||
|
|
<h2>Step 2.4 - Tweaking the Environment Observation Parameters<a class="headerlink" href="#step-2-4-tweaking-the-environment-observation-parameters" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>The arguments of interest are <code class="docutils literal notranslate"><span class="pre">include_cfrc_ext_in_observation</span></code> and <code class="docutils literal notranslate"><span class="pre">exclude_current_positions_from_observation</span></code>.</p>
|
||
|
|
<p>Here for <code class="docutils literal notranslate"><span class="pre">Go1</span></code> we have no particular reason to change them.</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span>
|
||
|
|
<span class="s1">'Ant-v5'</span><span class="p">,</span>
|
||
|
|
<span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">,</span>
|
||
|
|
<span class="n">forward_reward_weight</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">ctrl_cost_weight</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
|
||
|
|
<span class="n">contact_cost_weight</span><span class="o">=</span><span class="mf">5e-4</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_reward</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">main_body</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_z_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.195</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">),</span>
|
||
|
|
<span class="n">include_cfrc_ext_in_observation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># kept the game as the 'Ant' environment</span>
|
||
|
|
<span class="n">exclude_current_positions_from_observation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="c1"># kept the game as the 'Ant' environment</span>
|
||
|
|
<span class="n">reset_noise_scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
|
||
|
|
<span class="n">frame_skip</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
|
||
|
|
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
|
||
|
|
<span class="p">)</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
<p>Note: If you need additional observation elements (such as additional sensors), you can write your own <code class="docutils literal notranslate"><span class="pre">ObservationWrapper</span></code> (see the <a class="reference external" href="https://gymnasium.farama.org/main/api/wrappers/observation_wrappers/">documentation</a>).</p>
|
||
|
|
</section>
|
||
|
|
<section id="step-3-train-your-agent">
|
||
|
|
<h2>Step 3 - Train your Agent<a class="headerlink" href="#step-3-train-your-agent" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>Finally, we are done, we can use a RL algorithm to train an agent to walk/run the <code class="docutils literal notranslate"><span class="pre">Go1</span></code> robot.
|
||
|
|
Note: If you have followed this guide with your own robot model, you may discover during training that some environment parameters were not as desired, feel free to go back to step 2 and change anything as needed.</p>
|
||
|
|
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">gymnasium</span>
|
||
|
|
|
||
|
|
<span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span>
|
||
|
|
<span class="s1">'Ant-v5'</span><span class="p">,</span>
|
||
|
|
<span class="n">xml_file</span><span class="o">=</span><span class="s1">'./mujoco_menagerie/unitree_go1/scene.xml'</span><span class="p">,</span>
|
||
|
|
<span class="n">forward_reward_weight</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">ctrl_cost_weight</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
|
||
|
|
<span class="n">contact_cost_weight</span><span class="o">=</span><span class="mf">5e-4</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_reward</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">main_body</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
|
|
<span class="n">healthy_z_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.195</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">),</span>
|
||
|
|
<span class="n">include_cfrc_ext_in_observation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
|
|
<span class="n">exclude_current_positions_from_observation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||
|
|
<span class="n">reset_noise_scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
|
||
|
|
<span class="n">frame_skip</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
|
||
|
|
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
|
||
|
|
<span class="p">)</span>
|
||
|
|
<span class="o">...</span> <span class="c1"># run your RL algorithm</span>
|
||
|
|
</pre></div>
|
||
|
|
</div>
|
||
|
|
<p><img alt="image" src="https://github.com/Kallinteris-Andreas/Gymnasium-kalli/assets/30759571/bf1797a3-264d-47de-b14c-e3c16072f695" /></p>
|
||
|
|
<iframe id="odysee-iframe" style="width:100%; aspect-ratio:16 / 9;" src="https://odysee.com/$/embed/@Kallinteris-Andreas:7/video0-step-0-to-step-1000:1?r=6fn5jA9uZQUZXGKVpwtqjz1eyJcS3hj3" allowfullscreen></iframe>
|
||
|
|
<!--
|
||
|
|
Which can run up to `4.7 m/s` according to the manufacturer
|
||
|
|
-->
|
||
|
|
</section>
|
||
|
|
<section id="epilogue">
|
||
|
|
<h2>Epilogue<a class="headerlink" href="#epilogue" title="Link to this heading">¶</a></h2>
|
||
|
|
<p>You can follow this guide to create most quadruped environments.
|
||
|
|
To create humanoid/bipedal robots, you can also follow this guide using the <code class="docutils literal notranslate"><span class="pre">Gymnasium/MuJoCo/Humnaoid-v5</span></code> framework.</p>
|
||
|
|
<p>Author: <a class="reference external" href="https://github.com/Kallinteris-Andreas">@kallinteris-andreas</a></p>
|
||
|
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|
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<ul>
|
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<li><a class="reference internal" href="#">Load custom quadruped robot environments</a><ul>
|
||
|
|
<li><a class="reference internal" href="#setup">Setup</a></li>
|
||
|
|
<li><a class="reference internal" href="#step-0-1-download-a-robot-model">Step 0.1 - Download a Robot Model</a></li>
|
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|
|
<li><a class="reference internal" href="#step-1-load-the-model">Step 1 - Load the model</a></li>
|
||
|
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<li><a class="reference internal" href="#step-2-tweaking-the-environment-parameters">Step 2 - Tweaking the Environment Parameters</a></li>
|
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|
|
<li><a class="reference internal" href="#step-2-1-tweaking-the-environment-simulation-parameters">Step 2.1 - Tweaking the Environment Simulation Parameters</a></li>
|
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|
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<li><a class="reference internal" href="#step-2-2-tweaking-the-environment-termination-parameters">Step 2.2 - Tweaking the Environment Termination Parameters</a></li>
|
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|
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<li><a class="reference internal" href="#step-2-3-tweaking-the-environment-reward-parameters">Step 2.3 - Tweaking the Environment Reward Parameters</a></li>
|
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|
|
<li><a class="reference internal" href="#step-2-4-tweaking-the-environment-observation-parameters">Step 2.4 - Tweaking the Environment Observation Parameters</a></li>
|
||
|
|
<li><a class="reference internal" href="#step-3-train-your-agent">Step 3 - Train your Agent</a></li>
|
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|
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<li><a class="reference internal" href="#epilogue">Epilogue</a></li>
|
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</ul>
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|
Object.keys(subSections).forEach((subKey, i) => {
|
||
|
|
const subSectionElem = Object.assign(
|
||
|
|
document.createElement('div'), {
|
||
|
|
className:'farama-header-menu__subsection',
|
||
|
|
}
|
||
|
|
)
|
||
|
|
subSectionElem.appendChild(Object.assign(document.createElement('span'),
|
||
|
|
{
|
||
|
|
className:'farama-header-menu__subsection-title' ,
|
||
|
|
innerText: subKey
|
||
|
|
}
|
||
|
|
))
|
||
|
|
const ulElem = createProjectsList(subSections[subKey], key !== 'Foundation');
|
||
|
|
subSectionElem.appendChild(ulElem);
|
||
|
|
subSectionContainerElem.appendChild(subSectionElem);
|
||
|
|
})
|
||
|
|
sectionElem.appendChild(subSectionContainerElem);
|
||
|
|
} else {
|
||
|
|
const projects = sections[key];
|
||
|
|
const ulElem = createProjectsList(projects, true);
|
||
|
|
sectionElem.appendChild(ulElem);
|
||
|
|
}
|
||
|
|
menuContainer.appendChild(sectionElem)
|
||
|
|
});
|
||
|
|
}
|
||
|
|
|
||
|
|
xhr.onerror = function() {
|
||
|
|
console.error("Unable to load projects");
|
||
|
|
};
|
||
|
|
|
||
|
|
xhr.send();
|
||
|
|
</script>
|
||
|
|
|
||
|
|
|
||
|
|
<script>
|
||
|
|
const versioningConfig = {
|
||
|
|
githubUser: 'Farama-Foundation',
|
||
|
|
githubRepo: 'Gymnasium',
|
||
|
|
};
|
||
|
|
fetch('/main/_static/versioning/versioning_menu.html').then(response => {
|
||
|
|
if (response.status === 200) {
|
||
|
|
response.text().then(text => {
|
||
|
|
const container = document.createElement("div");
|
||
|
|
container.innerHTML = text;
|
||
|
|
document.querySelector("body").appendChild(container);
|
||
|
|
// innerHtml doenst evaluate scripts, we need to add them dynamically
|
||
|
|
Array.from(container.querySelectorAll("script")).forEach(oldScript => {
|
||
|
|
const newScript = document.createElement("script");
|
||
|
|
Array.from(oldScript.attributes).forEach(attr => newScript.setAttribute(attr.name, attr.value));
|
||
|
|
newScript.appendChild(document.createTextNode(oldScript.innerHTML));
|
||
|
|
oldScript.parentNode.replaceChild(newScript, oldScript);
|
||
|
|
});
|
||
|
|
});
|
||
|
|
} else {
|
||
|
|
console.warn("Unable to load versioning menu", response);
|
||
|
|
}
|
||
|
|
});
|
||
|
|
</script>
|
||
|
|
|
||
|
|
</body>
|
||
|
|
</html>
|