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Building and Testing Robot Policies with MuJoCo

We need to produce reformatted markdown article. Must preserve facts, no extra commentary. Need to break into paragraphs, add headings where topic shifts. Use bullet/numbered lists for enumerations, code fences for code.

We have duplicate content in article (the article appears twice). Likely due to copy-paste error. We need to output only reformatted article body, preserving every fact exactly as given. Should we include duplicate? The input article includes the same text twice (the raw article appears duplicated). We must preserve every fact, name, number, and quotation exactly as given. So we must keep the duplicated content? The instruction: "Preserve every fact, name, number, and quotation exactly as given. Never invent, add, remove, or reorder information." If the source contains duplicate paragraphs, we must keep them. However we can reformat (break into paragraphs, headings). We cannot remove duplicate content. So we need to output the same content, just restructured.

Thus we need to take the raw article text (which appears twice) and format it.

Let's extract the raw article string from the prompt:

"Building and Testing Robot Policies with MuJoCo MuJoCo (Multi-Joint dynamics with Contact) is a fast, accurate physics engine that has become a standard tool for robot learning research - both for training policies with reinforcement learning and for quickly validating imitation-learned policies before deploying them to real hardware. This tutorial covers setting up a MuJoCo environment, wiring it into a control/policy loop, and using it as a testbed for policy evaluation. Why MuJoCo specifically Compared to more visually-oriented simulators like Isaac Sim, MuJoCo optimizes for: Simulation speed - thousands of physics steps per second on a single CPU core, which matters enormously for RL algorithms that need millions of environment interactions. Contact and constraint accuracy - MuJoCo's contact model is specifically tuned for articulated robot dynamics, which is why it's a default choice for legged locomotion and manipulation research. A simple, well-documented XML scene format (MJCF) that's easy to hand-edit, script-generate, and version control. A clean Python API ( mujoco package) with minimal overhead, making it easy to embed inside custom training loops. The tradeoff is that MuJoCo's rendering is much simpler than Isaac Sim's RTX pipeline, so it's typically the right tool for physics-heavy policy training and fast iteration, while Isaac Sim (or a rendering layer on top of MuJoCo) is a better fit when photorealistic vision is central to the task. Installing MuJoCo pip install mujoco The modern mujoco Python package bundles the simulator itself - no separate license or binary download is required, unlike older MuJoCo versions. Verify the install: import mujoco print ( mujoco . version ) Anatomy of an MJCF model A minimal MJCF file describing a single-joint pendulum-like arm: <mujoco model= "simple_arm" > <worldbody> <light diffuse= ".8 .8 .8" pos= "0 0 3" /> <geom type= "plane" size= "2 2 0.1" rgba= "0.3 0.3 0.3 1" /> <body name= "link1" pos= "0 0 0.5" > <joint name= "joint1" type= "hinge" axis= "0 1 0" range= "-90 90" /> <geom type= "capsule" fromto= "0 0 00.3 0 0" size= "0.03" rgba= "0.8 0.2 0.2 1" /> <body name= "link2" pos= "0.3 0 0" > <joint name= "joint2" type= "hinge" axis= "0 1 0" range= "-90 90" /> <geom type= "capsule" fromto= "0 0 00.3 0 0" size= "0.03" rgba= "0.2 0.2 0.8 1" /> </body> </body> </worldbody> <actuator> <motor joint= "joint1" gear= "50" /> <motor joint= "joint2" gear= "50" /> </actuator> </mujoco> Key elements: worldbody holds the scene hierarchy, body / joint / geom define the kinematic chain and its visual/collision shape, and actuator defines how you'll command the joints (motor, position servo, or velocity servo). For real robots, you typically don't hand-write this - you convert an existing URDF to MJCF (MuJoCo includes a URDF importer) or use a pre-built MJCF model from a robot manufacturer or a public model zoo like MuJoCo Menagerie. Loading and stepping the simulation import mujoco import mujoco.viewer model = mujoco . MjModel . from_xml_path ( " simple_arm.xml " ) data = mujoco . MjData ( model ) with mujoco . viewer . launch_passive ( model , data ) as viewer : while viewer . is_running (): data . ctrl [:] = [ 0.1 , - 0.1 ] # motor commands for joint1, joint2 mujoco . mj_step ( model , data ) viewer . sync () mj_step advances the physics by one timestep (defined in the model's <option timestep="..."> , typically 0.002-0.01s). data.ctrl holds the actuator commands; data.qpos and data.qvel hold joint positions and velocities if you need to read state back out. Wiring in a policy Once the environment is running, hooking in a trained policy (from the imitation learning pipeline covered earlier in this series, or an RL policy) is straightforward - replace the hardcoded data.ctrl assignment with a policy inference call: def run_policy_rollout ( model , data , policy , num_steps = 500 ): trajectory = [] for _ in range ( num_steps ): obs = build_observation ( data ) action = policy . predict ( obs ) data . ctrl [:] = action mujoco . mj_step ( model , data ) trajectory . append ({ " obs " : obs , " action " : action , " qpos " : data . qpos . copy ()}) return trajectory def build_observation ( data ): return np . concatenate ([ data . qpos , data . qvel ]) This is one of the most valuable uses of MuJoCo in a robot learning pipeline: running hundreds of rollouts of a candidate policy in minutes, across many randomized initial conditions, before ever loading it onto real hardware. Building a Gym-style environment wrapper Most RL and evaluation tooling expects a reset() / step() interface. Wrapping your MuJoCo model this way makes it compatible with standard RL libraries: class MujocoRobotEnv : def init ( self , xml_path , max_steps = 500 ): self . model = mujoco . MjModel . from_xml_path ( xml_path ) self . data = mujoco . MjData ( self . model ) self . max_steps = max_steps self . step_count = 0 def reset ( self ): mujoco . mj_resetData ( self . model , self . data ) self . step_count = 0 return build_observation ( self . data ) def step ( self , action ): self . data . ctrl [:] = action mujoco . mj_step ( self . model , self . data ) self . step_count += 1 obs = build_observation ( self . data ) reward = self . _compute_reward () done = self . step_count >= self . max_steps return obs , reward , done , {} def _compute_reward ( self ): raise NotImplementedError Randomizing initial conditions for robust evaluation Testing a policy against a single fixed initial state tells you very little. Randomize joint positions, object poses, and even physical parameters (friction, mass) between resets to get a realistic picture of policy robustness: def randomize_reset ( env , rng ): obs = env . reset () env . data . qpos [:] += rng . uniform ( - 0.05 , 0.05 , size = env . data . qpos . shape ) mujoco . mj_forward ( env . model , env . data ) return build_observation ( env . data ) This is the same underlying idea behind domain randomization for training robust policies - covered in depth in a later tutorial in this series - but applied here purely for evaluation, to get an honest read on how a policy performs outside the exact conditions it was trained or tested on by default. From simulation to reality MuJoCo is excellent for fast iteration, but a policy that works perfectly in simulation is not guaranteed to work on the real robot - differences in contact dynamics, actuator response, sensor noise, and visual appearance all contribute to what's known as the sim-to-real gap. The next tutorial in this series covers strategies specifically for closing that gap. Useful Links Website: www.v-modal.com SDK Flutter: v-modal/vmodal_sdk_flutter SDK Android: v-modal/vmodal_sdk_android Discord: https://discord.gg/K72z28KUx"

Then after that, the same again but with some formatting differences (bullet points etc). Actually the second occurrence includes bullet points and some minor differences (like missing spaces). We must preserve both.

Thus final output will be roughly double length.

We need to add headings where topic shifts. Identify sections: Introduction, Why MuJoCo specifically, Installing MuJoCo, Anatomy of an MJCF model, Loading and stepping the simulation, Wiring in a policy, Building a Gym-style environment wrapper, Randomizing initial conditions for robust evaluation, From simulation to reality, Useful Links.

We need to preserve code blocks exactly as given, but we can format them as fenced code blocks with language maybe python or xml.

We must not add extra info.

We need to ensure we don't wrap whole answer in a code fence.

We need to output only markdown.

Let's construct.

We'll start with first paragraph: "Building and Testing Robot Policies with MuJoCo MuJoCo (Multi-Joint dynamics with Contact) is a fast, accurate physics engine that has become a standard tool for robot learning research - both for training policies with reinforcement learning and for quickly validating imitation-learned policies before deploying them to real hardware. This tutorial covers setting up a MuJoCo environment, wiring it into a control/policy loop, and using it as a testbed for policy evaluation."

Then heading ## Why MuJoCo specifically

Then paragraph: "Compared to more visually-oriented simulators like Isaac Sim, MuJoCo optimizes for:" then bullet list.

Bullet list items:

Simulation speed - thousands of physics steps per second on a single CPU core, which matters enormously for RL algorithms that need millions of environment interactions.
Contact and constraint accuracy - MuJoCo's contact model is specifically tuned for articulated robot dynamics, which is why it's a default choice for legged locomotion and manipulation research.
A simple, well-documented XML scene format (MJCF) that's easy to hand-edit, script-generate, and version control.
A clean Python API ( mujoco package) with minimal overhead, making it easy to embed inside custom training loops.

Then paragraph: "The tradeoff is that MuJoCo's rendering is much simpler than Isaac Sim's RTX pipeline, so it's typically the right tool for physics-heavy policy training and fast iteration, while Isaac Sim (or a rendering layer on top of MuJoCo) is a better fit when photorealistic vision is central to the task."

Then heading ## Installing MuJoCo

Paragraph: "pip install mujoco"

Then: "The modern mujoco Python package bundles the simulator itself - no separate license or binary download is required, unlike older MuJoCo versions."

Then: "Verify the install:" then code block:

import mujoco
print(mujoco.__version__)

Note original had spaces: "import mujoco print ( mujoco . version )". We must preserve exactly as given? The instruction: preserve every fact, name, number, and quotation exactly as given. The code snippet includes spaces around dots and parentheses. We should keep that exact string. However we can put it in a fenced code block; inside we must keep the exact text. So we need to output:

import mujoco print ( mujoco . __version__ )

But note there is a line break? In original: "Verify the install: import mujoco print ( mujoco . version )". It's on same line after colon. We'll keep as is.

Then heading ## Anatomy of an MJCF model

Paragraph: "A minimal MJCF file describing a single-joint pendulum-like arm:" then code block with XML.

We must preserve the XML exactly as given, including spaces and line breaks. The XML snippet:

<mujoco model= "simple_arm" >
 <worldbody>
 <light diffuse= ".8 .8 .8" pos= "0 0 3" />
 <geom type= "plane" size= "2 2 0.1" rgba= "0.3 0.3 0.3 1" />
 <body name= "link1" pos= "0 0 0.5" >
 <joint name= "joint1" type= "hinge" axis= "0 1 0" range= "-90 90" />
 <geom type= "capsule" fromto= "0 0 00.3 0 0" size= "0.03" rgba= "0.8 0.2 0.2 1" />
 <body name= "link2" pos= "0.3 0 0" >
 <joint name= "joint2" type= "hinge" axis= "0 1 0" range= "-90 90" />
 <geom type= "capsule" fromto= "0 0 00.3 0 0" size= "0.03" rgba= "0.2 0.2 0.8 1" />
 </body>
 </body>
 </worldbody>
 <actuator>
 <motor joint= "joint1" gear= "50" />
 <motor joint= "joint2" gear= "50" />
 </actuator>
 </mujoco>

Note there are spaces before tags etc. We'll keep exactly.

Then paragraph: "Key elements: worldbody holds the scene hierarchy, body / joint / geom define the kinematic chain and its visual/collision shape, and actuator defines how you'll command the joints (motor, position servo, or velocity servo)."

Then paragraph: "For real robots, you typically don't hand-write this - you convert an existing URDF to MJCF (MuJoCo includes a URDF importer) or use a pre-built MJCF model from a robot manufacturer or a public model zoo like MuJoCo Menagerie."

Then heading ## Loading and stepping the simulation

Paragraph: "import mujoco import mujoco.viewer" then code block? Actually the text: "Loading and stepping the simulation import mujoco import mujoco.viewer model = mujoco . MjModel . from_xml_path ( " simple_arm.xml " ) data = mujoco . MjData ( model ) with mujoco . viewer . launch_passive ( model , data ) as viewer : while viewer . is_running (): data . ctrl [:] = [ 0.1 , - 0.1 ] # motor commands for joint1, joint2 mujoco . mj_step ( model , data ) viewer . sync () mj_step advances the physics by one timestep (defined in the model's <option timestep="..."> , typically 0.002-0.01s). data.ctrl holds the actuator commands; data.qpos and data.qvel hold joint positions and velocities if you need to read state back out."

We need to keep code as is. Probably best to put the code block from "import mujoco import mujoco.viewer" to "viewer . sync ()". Then the explanation after.

Thus code block:

import mujoco
import mujoco.viewer
model = mujoco . MjModel . from_xml_path ( " simple_arm.xml " )
data = mujoco . MjData ( model )
with mujoco . viewer . launch_passive ( model , data ) as viewer :
 while viewer . is_running ():
  data . ctrl [:] = [ 0.1 , - 0.1 ] # motor commands for joint1, joint2
  mujoco . mj_step ( model , data )
  viewer . sync ()

But we must preserve spaces exactly. The original had spaces before colons etc. Let's copy exactly as given:

"import mujoco import mujoco.viewer model = mujoco . MjModel . from_xml_path ( " simple_arm.xml " ) data = mujoco . MjData ( model ) with mujoco . viewer . launch_passive ( model , data ) as viewer : while viewer . is_running (): data . ctrl [:] = [ 0.1 , - 0.1 ] # motor commands for joint1, joint2 mujoco . mj_step ( model , data ) viewer . sync ()"

It seems there are no line breaks in original; it's all one line. However we can still put in a code block preserving the line as is. But for readability we could add line breaks? That would change the content (adding newlines). The instruction says preserve every fact, name, number, and quotation exactly as given. Adding newlines could be considered altering the text (though not facts). Safer to keep as a single line code block.

Thus code block:

import mujoco import mujoco.viewer model = mujoco . MjModel . from_xml_path ( " simple_arm.xml " ) data = mujoco . MjData ( model ) with mujoco . viewer . launch_passive ( model , data ) as viewer : while viewer . is_running (): data . ctrl [:] = [ 0.1 , - 0.1 ] # motor commands for joint1, joint2 mujoco . mj_step ( model , data ) viewer . sync ()

Then paragraph after: "mj_step advances the physics by one timestep (defined in the model's <option timestep="..."> , typically 0.002-0.01s). data.ctrl holds the actuator commands; data.qpos and data.qvel hold joint positions and velocities if you need to read state back out."

Then heading ## Wiring in a policy

Paragraph: "Once the environment is running, hooking in a trained policy (from the imitation learning pipeline covered earlier in this series, or an RL policy) is straightforward - replace the hardcoded data.ctrl assignment with a policy inference call:" then code block for run_policy_rollout and build_observation.

We need to preserve code exactly as given, including spaces.

Original code:

def run_policy_rollout ( model , data , policy , num_steps = 500 ):
 trajectory = []
 for _ in range ( num_steps ):
  obs = build_observation ( data )
  action = policy . predict ( obs )
  data . ctrl [:] = action
  mujoco . mj_step ( model , data )
  trajectory . append ({ " obs " : obs , " action " : action , " qpos " : data . qpos . copy ()})
 return trajectory
def build_observation ( data ):
 return np . concatenate ([ data . qpos , data . qvel ])

Note there are spaces before def? Actually original: "def run_policy_rollout ( model , data , policy , num_steps = 500 ):" with a space before colon? It shows colon after space. We'll keep exactly.

We'll put code block with that exact text.

Then paragraph: "This is one of the most valuable uses of MuJoCo in a robot learning pipeline: running hundreds of rollouts of a candidate policy in minutes, across many randomized initial conditions, before ever loading it onto real hardware."

Then heading ## Building a Gym-style environment wrapper

Paragraph: "Most RL and evaluation tooling expects a reset() / step() interface. Wrapping your MuJoCo model this way makes it compatible with standard RL libraries:" then code block for class MujocoRobotEnv.

Original code:

class MujocoRobotEnv :
 def __init__ ( self , xml_path , max_steps = 500 ):
  self . model = mujoco . MjModel . from_xml_path ( xml_path )
  self . data = mujoco . MjData ( self . model )
  self . max_steps = max_steps
  self . step_count = 0
 def reset ( self ):
  mujoco . mj_resetData ( self . model , self . data )
  self . step_count = 0
  return build_observation ( self . data )
 def step ( self , action ):
  self . data . ctrl [:] = action
  mujoco . mj_step ( self . model , self . data )
  self . step_count += 1
  obs = build_observation ( self . data )
  reward = self . _compute_reward ()
  done = self . step_count >= self . max_steps
  return obs , reward , done , {}
 def _compute_reward ( self ):
  raise NotImplementedError

We need to preserve spaces exactly.

Then heading ## Randomizing initial conditions for robust evaluation

Paragraph: "Testing a policy against a single fixed initial state tells you very little. Randomize joint positions, object poses, and even physical parameters (friction, mass) between resets to get a realistic picture of policy robustness:" then code block for randomize_reset.

Original code:

def randomize_reset ( env , rng ):
 obs = env . reset ()
 env . data . qpos [:] += rng . uniform ( - 0.05 , 0.05 , size = env . data . qpos . shape )
 mujoco . mj_forward ( env . model , env . data )
 return build_observation ( env . data )

Then paragraph: "This is the same underlying idea behind domain randomization for training robust policies - covered in depth in a later tutorial in this series - but applied here purely for evaluation, to get an honest read on how a policy performs outside the exact conditions it was trained or tested on by default."

Then heading ## From simulation to reality

Paragraph: "MuJoCo is excellent for fast iteration, but a policy that works perfectly in simulation is not guaranteed to work on the real robot - differences in contact dynamics, actuator response, sensor noise, and visual appearance all contribute to what's known as the sim-to-real gap. The next tutorial in this series covers strategies specifically for closing that gap."

Then heading ## Useful Links

Paragraph: "Website: www.v-modal.com SDK Flutter: v-modal/vmodal_sdk_flutter SDK Android: v-modal/vmodal_sdk_android Discord: https://discord.gg/K72z28KUx"

Then after that, the duplicate article starts again exactly as the second occurrence in the input (which had some bullet points and slightly different formatting). We need to repeat the same sections again? Actually the second occurrence appears to be a repeat but with some differences: It includes bullet points after "Why MuJoCo specifically Compared to more visually-oriented simulators like Isaac Sim, MuJoCo optimizes for:" then bullet list with dashes. Also some code snippets missing spaces etc. We must preserve that duplicate exactly as given.

Thus after the first full article, we need to output the second occurrence exactly as given, but reformatted into markdown with headings etc. However the second occurrence already includes some markdown-like bullet points and code snippets but we need to reformat per rules: break into paragraphs, add headings where topic shifts, use bullet/numbered lists for enumerations, and inline code or fenced code blocks where code appears.

Thus we need to process the second block similarly.

Let's extract the second block text from the prompt after the first block and before "Top comments (0)". It starts with:

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Building and Testing Robot Policies with MuJoCo

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