Add support for MuJoCo #597
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The migration to gen_param_lib broke the way params are loaded when we use the pywrapper, this fixes it.
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Important Review skippedDraft detected. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughThe pull request introduces several modifications across various components of the Changes
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Actionable comments posted: 22
🧹 Outside diff range and nitpick comments (12)
bitbots_wolfgang/wolfgang_description/urdf/scene.xml (3)
1-2: LGTM! Consider adding a comment for the include statement.The root element correctly defines the MuJoCo model for the "wolfgang scene". The modular approach of including the robot definition from a separate file is a good practice.
Consider adding a brief comment above the include statement to explain what "robot.xml" contains:
<mujoco model="wolfgang scene"> + <!-- Include the Wolfgang robot model definition --> <include file="robot.xml"/>
6-10: LGTM! Consider adding comments for clarity.The visual settings are well-defined, providing a balanced lighting setup with a slight atmospheric effect and appropriate global lighting direction.
For improved readability, consider adding brief comments explaining the purpose of each setting:
<visual> + <!-- Balanced lighting with no specular reflection --> <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/> + <!-- Slight atmospheric haze effect --> <rgba haze="0.15 0.25 0.35 1"/> + <!-- Global lighting direction for shadow and depth --> <global azimuth="160" elevation="-20"/> </visual>
12-17: LGTM! Consider adding a more realistic ground texture.The asset definitions are well-structured, providing a simple skybox and a checker pattern for the ground. This setup is functional and provides good visual references.
For enhanced realism, consider using a more detailed ground texture. You could add an option to switch between the checker pattern and a more realistic texture:
<asset> <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/> <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3" markrgb="0.8 0.8 0.8" width="300" height="300"/> + <texture type="2d" name="realistic_ground" file="path/to/realistic_ground_texture.png" /> <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/> + <material name="realistic_groundplane" texture="realistic_ground" texuniform="true" texrepeat="5 5" reflectance="0.2"/> </asset>Then, you can switch between these materials in the worldbody section as needed.
bitbots_motion/bitbots_quintic_walk/include/bitbots_quintic_walk/walk_pywrapper.hpp (1)
26-27: Approve constructor changes with documentation suggestion.The updated constructor signature improves clarity and functionality by:
- Adding support for MoveIt parameters, which aligns with the PR objective of integrating MuJoCo.
- Renaming
parameter_msgstowalk_parameter_msgsfor better specificity.These changes maintain backward compatibility while allowing for more flexible configuration.
Consider updating the class documentation to explain the purpose and expected content of both
walk_parameter_msgsandmoveit_parameter_msgs. This will help users understand how to properly utilize these parameters.bitbots_motion/bitbots_quintic_walk/bitbots_quintic_walk_py/py_walk.py (1)
16-33: LGTM: Good refactoring for parameter handling.The changes to the
__init__method improve the flexibility and clarity of parameter handling:
- Separating
walk_parametersandmoveit_parametersallows for more precise control.- The use of
Optional[list[Parameter]]is consistent with the new import and improves type safety.- The
serialize_parametersinner function is a good encapsulation of the serialization logic.One minor suggestion:
Consider adding type hints to the
serialize_parametersfunction for consistency:def serialize_parameters(parameters: Optional[list[Parameter]]) -> list: if parameters is None: return [] return list(map(serialize_message, parameters))This change would make the type hints consistent throughout the method.
bitbots_wolfgang/wolfgang_description/urdf/robot.xml (2)
1-22: LGTM! Consider adding comments for better readability.The model declaration and default settings are well-structured and appropriate for a humanoid robot. The use of different classes for various components (collision, visual, and motor types) allows for easy property assignment.
Consider adding comments to explain the purpose of each default class and the compiler settings. This would improve code readability and make it easier for other developers to understand and maintain the model.
<mujoco model="wolfgang"> <default> + <!-- Collision settings --> <default class="collision"> <geom group="3"/> </default> + <!-- Visual settings --> <default class="visual"> <geom material="black" contype="0" conaffinity="0" group="2"/> </default> + <!-- Motor settings for different types --> <default class="mx106"> <joint damping="1.084" armature="0.045" frictionloss="2.55"/> <position kp="50.1" ctrlrange="-3.141592 3.141592" forcerange="-50 50"/> </default> <default class="mx64"> <joint damping="1.084" armature="0.045" frictionloss="2.55"/> <position kp="50.1" ctrlrange="-3.141592 3.141592" forcerange="-50 50"/> </default> <default class="xh-540"> <joint damping="1.084" armature="0.045" frictionloss="0.03"/> <position kp="50.1" ctrlrange="-3.141592 3.141592" forcerange="-50 50"/> </default> </default> + <!-- Compiler settings: use radians and enable auto limits --> <compiler angle="radian" autolimits="true"/>
1-612: Well-defined model with opportunities for enhancement.Overall, this MuJoCo model for the "wolfgang" humanoid robot is comprehensive and well-structured. It provides a detailed representation of the robot's physical structure and actuator system. The use of default classes for different components and motor types is a good practice for maintaining consistency and ease of modification.
The model is functional and appears to accurately represent the robot's structure and capabilities.
To further improve this model, consider the following suggestions:
Performance optimization: Evaluate the complexity of the collision geometries. In some cases, simplified collision shapes could be used to improve simulation performance without significantly affecting accuracy.
Parameterization: Consider introducing parameters for key dimensions or properties that might need to be adjusted frequently. This could be implemented using MuJoCo's macro system or through pre-processing scripts.
Validation: Implement a validation process to ensure that the model behaves as expected. This could include unit tests for individual components and integration tests for the full model.
Documentation: Add overall documentation for the model, explaining its purpose, key features, and any assumptions or limitations. This could be included as comments at the top of the file or in a separate README document.
Version control: If not already in place, ensure that this model is under version control, and consider implementing a change log to track modifications over time.
Collaboration: If multiple team members work on this model, consider establishing coding guidelines specific to MuJoCo models to ensure consistency across contributors.
By implementing these suggestions, you can enhance the long-term maintainability, flexibility, and reliability of your "wolfgang" robot model.
mujoco_env.py (1)
85-89: Ensure OpenCV windows are properly closed inclosemethod.To prevent OpenCV windows from remaining open after the environment is closed, consider adding
cv2.destroyAllWindows()in theclosemethod.Apply this diff:
def close(self): super().close() # Explicitly delete the objects to free memory and get a more deterministic behavior del self.data del self.renderer del self.model + cv2.destroyAllWindows()This ensures that all OpenCV windows are closed when the environment is closed.
bitbots_wolfgang/wolfgang_description/scripts/urdf_to_mujoco.py (2)
84-85: Consider using logging instead of print statements.Using the
loggingmodule instead ofSuggested code change:
+import logging +logging.basicConfig(level=logging.INFO) ... if not os.path.exists(input_urdf): - print('Error: URDF file not found:', input_urdf) + logging.error('URDF file not found: %s', input_urdf) sys.exit(1) ... - print('Saved MuJoCo model to', output_filename) + logging.info('Saved MuJoCo model to %s', output_filename)
108-110: Address the TODO comments regarding the 'torso' body.There are TODO notes to check the position of the 'torso' body and investigate why it's not the root body. Resolving these will ensure the model accurately reflects the robot's structure.
Do you need assistance in verifying the position and structure of the 'torso' body? I can help by suggesting methods to validate or adjust the model accordingly.
bitbots_motion/bitbots_quintic_walk/src/walk_pywrapper.cpp (1)
31-33: Use professional language in commentsThe comment includes informal language ("does some bullshit"), which may be considered unprofessional. For better maintainability and professionalism, consider rephrasing the comment to use formal language.
Suggested revision:
- // We pass it the node we created. But the walking also creates a helper node for moveit (otherwise dynamic - // reconfigure does not work, because moveit does some bullshit with their parameter declarations leading dynamic - // reconfigure not working). This way the walking parameters are isolated from the moveit parameters, allowing dynamic - // reconfigure to work. Therefore we need to pass the moveit parameters to the walking. + // We pass it the node we created. However, the walking also creates a helper node for MoveIt because dynamic + // reconfigure does not work due to MoveIt's handling of parameter declarations. This way, the walking parameters + // are isolated from the MoveIt parameters, allowing dynamic reconfigure to work. Therefore, we need to pass the + // MoveIt parameters to the walking.bitbots_motion/bitbots_quintic_walk/src/walk_node.cpp (1)
21-25: Rephrase unprofessional language and ensure consistent capitalization in commentsThe comments in lines 21-25 contain informal language ("moveit does some bullshit with parameter declarations"). It's important to maintain professional language in code comments. Additionally, 'moveit' should be consistently capitalized as 'MoveIt' to match the official project naming.
Apply the following diff to rephrase the comments and correct capitalization:
- // Create dummy node for moveit. This is necessary for dynamic reconfigure to work (moveit does some bullshit with - // parameter declarations, so we need to isolate the walking parameters from the moveit parameters). - // If the walking is instantiated using the python wrapper, moveit parameters are passed because no moveit config - // is loaded in the conventional way. Normally the moveit config is loaded via launch file and the passed vector is - // empty. + // Create a dummy node for MoveIt. This is necessary for dynamic reconfigure to work. MoveIt handles + // parameter declarations in a non-standard way, so we need to isolate the walking parameters from the MoveIt parameters. + // If the walking is instantiated using the Python wrapper, MoveIt parameters are passed because no MoveIt config + // is loaded in the conventional way. Normally, the MoveIt config is loaded via a launch file and the passed vector is + // empty.
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📒 Files selected for processing (11)
- bitbots_motion/bitbots_quintic_walk/bitbots_quintic_walk_py/py_walk.py (2 hunks)
- bitbots_motion/bitbots_quintic_walk/include/bitbots_quintic_walk/walk_node.hpp (1 hunks)
- bitbots_motion/bitbots_quintic_walk/include/bitbots_quintic_walk/walk_pywrapper.hpp (1 hunks)
- bitbots_motion/bitbots_quintic_walk/src/walk_node.cpp (1 hunks)
- bitbots_motion/bitbots_quintic_walk/src/walk_pywrapper.cpp (2 hunks)
- bitbots_wolfgang/wolfgang_description/CMakeLists.txt (1 hunks)
- bitbots_wolfgang/wolfgang_description/scripts/urdf_to_mujoco.py (1 hunks)
- bitbots_wolfgang/wolfgang_description/urdf/robot.xml (1 hunks)
- bitbots_wolfgang/wolfgang_description/urdf/scene.xml (1 hunks)
- mujoco_env.py (1 hunks)
- sb3_mujoco.py (1 hunks)
🧰 Additional context used
Ruff
mujoco_env.py
8-8:
numpyimported but unusedRemove unused import:
numpy(F401)
sb3_mujoco.py
3-3:
numpyimported but unusedRemove unused import:
numpy(F401)
6-6:
stable_baselines3.common.vec_env.SubprocVecEnvimported but unusedRemove unused import:
stable_baselines3.common.vec_env.SubprocVecEnv(F401)
🔇 Additional comments not posted (12)
bitbots_wolfgang/wolfgang_description/urdf/scene.xml (2)
4-4: LGTM! Statistic element is well-defined.The statistic element correctly defines the center and extent for the scene, which will help in setting up the camera view and scaling in the MuJoCo viewer. The values (center at 0,0,0.2 and extent of 0.6) seem appropriate for a humanoid robot scene.
19-22: LGTM, but verify the floor size.The worldbody structure is correct, with appropriate positioning of the directional light and use of the groundplane material for the floor.
However, the floor size is set to "0 0 0.05", which might not be intentional. Usually, the first two values define the horizontal extent of the plane. A size of "0 0 0.05" would create a plane with no horizontal extent, which may not be visible or functional in the simulation.
Consider adjusting the floor size to cover an appropriate area for your simulation. For example:
- <geom name="floor" size="0 0 0.05" type="plane" material="groundplane"/> + <geom name="floor" size="10 10 0.05" type="plane" material="groundplane"/>This change would create a 20m x 20m floor (extending 10m in each direction from the center) with a thickness of 0.05m. Adjust these values based on your specific requirements.
bitbots_wolfgang/wolfgang_description/CMakeLists.txt (1)
62-62: LGTM: New installation directive for scripts directory.The added installation directive
install(DIRECTORY scripts DESTINATION share/${PROJECT_NAME})is correctly formatted and placed appropriately within the CMakeLists.txt file. This change ensures that the contents of thescriptsdirectory are included in the installation process, which aligns with the project's structure and the PR's objective of adding support for MuJoCo.To ensure the
scriptsdirectory exists and contains relevant files, please run the following script:This script will help verify the existence of the
scriptsdirectory and list its contents, particularly focusing on any MuJoCo-related scripts that might have been added as part of the MuJoCo support implementation.✅ Verification successful
Verified: MuJoCo-related scripts are present in the scripts directory.
The added installation directive
install(DIRECTORY scripts DESTINATION share/${PROJECT_NAME})correctly includes thescriptsdirectory, ensuring that all necessary MuJoCo scripts are installed as part of the project.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence and contents of the scripts directory # Check if the scripts directory exists if [ -d "scripts" ]; then echo "scripts directory exists" # List contents of the scripts directory echo "Contents of scripts directory:" ls -l scripts else echo "scripts directory does not exist" fi # Check for any MuJoCo-related scripts echo "MuJoCo-related scripts:" fd -t f . scripts | rg -i "mujoco"Length of output: 839
Script:
#!/bin/bash # Description: Search for 'mujoco' mentions in the scripts directory and relevant configuration files # Search for 'mujoco' within the scripts directory echo "Searching for 'mujoco' in scripts directory:" rg -i 'mujoco' scripts # Additionally, search CMakeLists.txt for any 'mujoco' mentions echo "Searching for 'mujoco' in CMakeLists.txt:" rg -i 'mujoco' bitbots_wolfgang/wolfgang_description/CMakeLists.txtLength of output: 294
Script:
#!/bin/bash # Description: Perform a comprehensive search for 'mujoco' across the entire repository echo "Searching for 'mujoco' in the entire codebase:" rg -i 'mujoco'Length of output: 2443
bitbots_motion/bitbots_quintic_walk/include/bitbots_quintic_walk/walk_pywrapper.hpp (1)
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1-58: File review summary and follow-up suggestions.The changes to this file are focused on the
PyWalkWrapperconstructor, which now includes support for MoveIt parameters. This modification aligns well with the PR objective of adding MuJoCo support.To ensure the changes are fully integrated and maintain the overall code quality:
- Verify that all calls to the
PyWalkWrapperconstructor throughout the codebase have been updated to accommodate the new parameter.- Consider adding unit tests to verify the correct handling of both
walk_parameter_msgsandmoveit_parameter_msgs.- Update any relevant documentation or README files to reflect these new capabilities.
To check for potential issues with constructor usage, run the following script:
This will help identify any places where the constructor is called and may need updating.
bitbots_motion/bitbots_quintic_walk/bitbots_quintic_walk_py/py_walk.py (2)
1-1: LGTM: Good addition of type hinting.The import of
Optionalfromtypingis a positive change. It allows for more precise type hinting, which can improve code readability and enable better static type checking.
Line range hint
1-33: Summary: Well-implemented changes for improved parameter handling.The modifications to the
PyWalkclass, particularly in the__init__method, represent a significant improvement in parameter handling and type safety:
- The separation of
walk_parametersandmoveit_parametersallows for more precise control and better organization of configuration options.- The use of
Optional[list[Parameter]]and the import ofOptionalfromtypingenhance type safety and code readability.- The introduction of the
serialize_parametersinner function encapsulates the serialization logic, promoting cleaner code organization.These changes appear to be backwards compatible, as the rest of the file remains unchanged and should work seamlessly with the new parameter structure. The modifications contribute to a more robust and flexible implementation of the
PyWalkclass.Overall, this is a well-executed refactoring that improves code quality without introducing breaking changes.
bitbots_motion/bitbots_quintic_walk/include/bitbots_quintic_walk/walk_node.hpp (3)
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1-232: Summary of changes and potential impact.The changes in this file are focused on two main areas:
- The constructor parameter rename from
parameterstomoveit_parameters.- The removal of the
onSetParametersmethod.These changes suggest a shift in how parameters, particularly those related to MoveIt, are handled in the
WalkNodeclass. While the overall structure of the class remains intact, these modifications may have implications for:
- Initialization of
WalkNodeobjects- Dynamic parameter reconfiguration
- Integration with the MoveIt system
To ensure system stability and correct functionality:
- Review and update all code that instantiates
WalkNodeobjects.- Verify that parameter management is handled correctly throughout the class.
- Ensure that the integration with MoveIt is properly maintained.
- Update any related documentation or comments to reflect these changes.
The changes appear to be intentional and part of a larger refactoring effort. However, careful testing of the affected components is recommended to prevent any regressions in the robot motion control system.
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1-232: Address the removal of theonSetParametersmethod.The
onSetParametersmethod has been removed from theWalkNodeclass. This change may affect how parameters are set and managed within the class, potentially impacting dynamic reconfiguration of parameters.
- Ensure that any code relying on the
onSetParametersmethod is updated or removed.- If dynamic parameter reconfiguration is still needed, implement an alternative method or use the ROS 2 parameter system directly.
- Update any documentation or comments related to parameter setting in this class.
To check for potential issues related to this removal, run the following script:
#!/bin/bash # Search for any remaining references to onSetParameters rg --type cpp "\bonSetParameters\b" bitbots_motion/bitbots_quintic_walk # Search for potential parameter setting code that might need updating rg --type cpp "set_parameters|declare_parameter" bitbots_motion/bitbots_quintic_walk
57-57: Verify the impact of the constructor parameter name change.The constructor parameter name has been changed from
parameterstomoveit_parameters. This change suggests a more specific use of the parameter, likely related to MoveIt configuration.
- Ensure that all calls to this constructor in the codebase are updated to reflect this change.
- Update any related documentation or comments to clarify the purpose of
moveit_parameters.- Verify that the usage of this parameter within the constructor and the rest of the class is consistent with the new name.
To check for potential issues, run the following script:
✅ Verification successful
Constructor parameter renaming verified successfully.
All instances of the constructor parameter have been updated to
moveit_parameters, and remaining usages ofparameterin the codebase pertain to different contexts and do not cause conflicts.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for constructor calls that might need updating rg --type cpp "WalkNode\s*\([^)]*\)" -g '!walk_node.hpp' # Search for any references to the old parameter name that might need updating rg --type cpp "\bparameters\b" bitbots_motion/bitbots_quintic_walkLength of output: 453
Script:
#!/bin/bash # Search for all instances of 'parameter_overrides' and 'declare_parameter' to verify usage of 'moveit_parameters' rg --type cpp 'parameter_overrides' bitbots_motion/bitbots_quintic_walk rg --type cpp 'declare_parameter' bitbots_motion/bitbots_quintic_walkLength of output: 760
Script:
#!/bin/bash # Search for all instances of 'moveit_parameters' to verify consistent usage rg --type cpp 'moveit_parameters' bitbots_motion/bitbots_quintic_walk # Search for any additional instances of 'parameter' that may relate to the constructor change rg --type cpp '\bparameter\b' bitbots_motion/bitbots_quintic_walkLength of output: 2587
mujoco_env.py (1)
50-50: Verify correctness of observation slicing in_get_obs.In the
_get_obsmethod, observations are obtained by slicing the position vector:return self.data.qpos[self.joint_id_observation_start[1]:]Ensure that this slicing accurately captures all relevant joint positions for the observation. If the model changes or additional joints are added, the slicing may need to be adjusted.
bitbots_wolfgang/wolfgang_description/scripts/urdf_to_mujoco.py (1)
36-36: Verify the actuator mapping.There's a TODO to verify the
ACTUATOR_MAPPINGfor correctness. Ensuring the mapping aligns with actual hardware specifications is crucial for accurate simulation.Would you like assistance in verifying the actuator mappings?
bitbots_motion/bitbots_quintic_walk/src/walk_pywrapper.cpp (1)
5-6: Update constructor usage to match new signatureThe
PyWalkWrapperconstructor now includes an additional parameterstd::vector<py::bytes> moveit_parameter_msgs. Please ensure that all instances wherePyWalkWrapperis instantiated are updated to reflect this new signature and that the appropriatemoveit_parameter_msgsare provided.To verify that all instantiations have been updated, run the following script:
✅ Verification successful
All instances of
PyWalkWrapperhave been updated to match the new constructor signature.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Find all instantiations of PyWalkWrapper to ensure they use the updated constructor. # Search for instances of PyWalkWrapper instantiation rg --type cpp --type c++ --type cxx 'PyWalkWrapper\(' -A 2Length of output: 88
Script:
#!/bin/bash # Description: Find all instantiations of PyWalkWrapper to ensure they use the updated constructor. # Search for instances of PyWalkWrapper instantiation rg --type cpp --type cxx 'PyWalkWrapper\(' -A 2Length of output: 77
Script:
#!/bin/bash # Description: Find all instantiations of PyWalkWrapper to ensure they use the updated constructor. # Search for instances of PyWalkWrapper instantiation in C++ files rg 'PyWalkWrapper\(' --glob '*.cpp' --glob '*.cxx' --glob '*.cc' --glob '*.hpp' --glob '*.hxx' --glob '*.hh' -A 2Length of output: 1099
| from stable_baselines3 import PPO | ||
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| display_env = make_vec_env(WolfgangMujocoEnv, n_envs=1) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider increasing the number of environment instances.
The current setup creates only one environment instance (n_envs=1). While this is valid, increasing the number of environments can potentially improve training performance by allowing parallel sampling of experiences.
Consider modifying the line as follows to create multiple environment instances:
display_env = make_vec_env(WolfgangMujocoEnv, n_envs=4) # or another appropriate numberThe optimal number of environments depends on your available computational resources and the complexity of the WolfgangMujocoEnv. Experiment with different values to find the best balance between performance and resource usage.
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| display_env = make_vec_env(WolfgangMujocoEnv, n_envs=1) | ||
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| model = PPO("MlpPolicy", display_env, verbose=1, n_steps=512) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Review PPO hyperparameters for optimal performance.
The current PPO model setup uses reasonable default values, but consider the following points:
- The
verbose=1setting is useful for debugging but may impact performance in production. Consider setting it to 0 for production use. - The
n_steps=512parameter determines the number of steps to run for each environment per update. This value might need tuning based on your specific environment and problem.
Consider adjusting the model creation as follows:
model = PPO("MlpPolicy", display_env, verbose=0, n_steps=512) # Adjust verbose and n_steps as neededIt's recommended to experiment with different n_steps values (e.g., 256, 1024, 2048) to find the optimal setting for your specific use case. Additionally, you might want to consider other hyperparameters like learning rate, batch size, and number of epochs for further optimization.
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| model = PPO("MlpPolicy", display_env, verbose=1, n_steps=512) | ||
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| model.learn(20000, log_interval=1) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Optimize training duration and logging frequency.
The current training setup might benefit from the following adjustments:
- The training duration (20,000 steps) might be insufficient for complex environments. Consider increasing this value based on your environment's complexity and the model's performance.
- Logging at every step (
log_interval=1) can be computationally expensive and produce excessive output. A larger interval might be more practical.
Consider modifying the training call as follows:
model.learn(total_timesteps=100000, log_interval=100) # Adjust values as neededExperiment with different total_timesteps values to ensure the model reaches satisfactory performance. Monitor the training progress and adjust as necessary. For the log_interval, a value between 10 and 100 often provides a good balance between information and performance.
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| obs = display_env.reset() | ||
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| for i in range(1000): | ||
| action, _state = model.predict(obs, deterministic=True) | ||
| obs, reward, done, info = display_env.step(action) | ||
| if done: | ||
| obs = display_env.reset() | ||
| if i % 10 == 0: | ||
| display_env.render("human") | ||
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| display_env.close() |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance evaluation process with result analysis and model saving.
The current evaluation process is well-structured, but consider the following enhancements:
- Add result analysis to quantify the model's performance.
- Consider saving the trained model for future use.
- Optionally, add a mechanism to early stop the evaluation if a certain performance threshold is met.
Here's a suggested modification to incorporate these enhancements:
import numpy as np
# ... (previous code remains the same)
model.learn(total_timesteps=100000, log_interval=100)
# Save the trained model
model.save("ppo_wolfgang_mujoco")
# Evaluation
display_env = model.get_env()
obs = display_env.reset()
episode_rewards = []
episode_lengths = []
current_episode_reward = 0
current_episode_length = 0
for i in range(1000):
action, _state = model.predict(obs, deterministic=True)
obs, reward, done, info = display_env.step(action)
current_episode_reward += reward
current_episode_length += 1
if done:
episode_rewards.append(current_episode_reward)
episode_lengths.append(current_episode_length)
current_episode_reward = 0
current_episode_length = 0
obs = display_env.reset()
if i % 10 == 0:
display_env.render("human")
display_env.close()
# Analyze results
print(f"Mean episode reward: {np.mean(episode_rewards):.2f} +/- {np.std(episode_rewards):.2f}")
print(f"Mean episode length: {np.mean(episode_lengths):.2f} +/- {np.std(episode_lengths):.2f}")This modification adds reward and length tracking for each episode, saves the trained model, and provides basic statistical analysis of the evaluation results. Consider expanding on this analysis based on your specific performance metrics and requirements.
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| from stable_baselines3.common.vec_env import SubprocVecEnv | ||
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| from stable_baselines3 import PPO |
There was a problem hiding this comment.
Remove unused import and consider numpy usage.
- The
SubprocVecEnvimport is unused and can be safely removed. - The
numpyimport is not directly used in the visible code. If it's not used indirectly (e.g., by imported modules), consider removing it as well.
Apply this diff to address the unused imports:
from mujoco_env import WolfgangMujocoEnv
-import numpy as np
from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3 import PPOIf numpy is used indirectly, please add a comment explaining its necessity to improve code clarity.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from mujoco_env import WolfgangMujocoEnv | |
| import numpy as np | |
| from stable_baselines3.common.env_util import make_vec_env | |
| from stable_baselines3.common.vec_env import SubprocVecEnv | |
| from stable_baselines3 import PPO | |
| from mujoco_env import WolfgangMujocoEnv | |
| from stable_baselines3.common.env_util import make_vec_env | |
| from stable_baselines3 import PPO |
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3-3:
numpyimported but unusedRemove unused import:
numpy(F401)
6-6:
stable_baselines3.common.vec_env.SubprocVecEnvimported but unusedRemove unused import:
stable_baselines3.common.vec_env.SubprocVecEnv(F401)
| temp_xml_file_path = tempfile.mktemp() | ||
| mujoco.mj_saveLastXML(temp_xml_file_path, model) |
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Avoid using deprecated tempfile.mktemp().
tempfile.mktemp() is deprecated and insecure due to potential race conditions. Use tempfile.NamedTemporaryFile() or tempfile.mkstemp() to safely create a temporary file.
Suggested code change:
- temp_xml_file_path = tempfile.mktemp()
- mujoco.mj_saveLastXML(temp_xml_file_path, model)
+ with tempfile.NamedTemporaryFile(suffix='.xml', delete=False) as temp_file:
+ mujoco.mj_saveLastXML(temp_file.name, model)
+ temp_xml_file_path = temp_file.name📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| temp_xml_file_path = tempfile.mktemp() | |
| mujoco.mj_saveLastXML(temp_xml_file_path, model) | |
| with tempfile.NamedTemporaryFile(suffix='.xml', delete=False) as temp_file: | |
| mujoco.mj_saveLastXML(temp_file.name, model) | |
| temp_xml_file_path = temp_file.name |
| joint.attrib.pop('frictionloss', None) | ||
| joint.attrib.pop('limited', None) | ||
| # Add class based on the actuator type | ||
| joint.attrib['class'] = ACTUATOR_MAPPING[joint.attrib['name']] |
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Handle missing joint names in ACTUATOR_MAPPING to prevent KeyError.
Assigning the class attribute without checking if the joint name exists in ACTUATOR_MAPPING may raise a KeyError. Add error handling to manage joints not present in the mapping.
Suggested code change:
- joint.attrib['class'] = ACTUATOR_MAPPING[joint.attrib['name']]
+ actuator_class = ACTUATOR_MAPPING.get(joint.attrib['name'])
+ if actuator_class:
+ joint.attrib['class'] = actuator_class
+ else:
+ print(f"Warning: Joint '{joint.attrib['name']}' not in ACTUATOR_MAPPING.")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| joint.attrib['class'] = ACTUATOR_MAPPING[joint.attrib['name']] | |
| actuator_class = ACTUATOR_MAPPING.get(joint.attrib['name']) | |
| if actuator_class: | |
| joint.attrib['class'] = actuator_class | |
| else: | |
| print(f"Warning: Joint '{joint.attrib['name']}' not in ACTUATOR_MAPPING.") |
|
|
||
| # This script converts a URDF file to a MuJoCo XML model file. | ||
|
|
||
| INPUT_URDF = '/home/florian/Projekt/bitbots/bitbots_main/bitbots_wolfgang/wolfgang_description/urdf/robot.urdf' |
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🛠️ Refactor suggestion
Make the URDF input file path configurable.
Hardcoding the URDF file path limits the script's portability. Consider accepting the input file path as a command-line argument or configuration parameter.
Suggested code change:
+import argparse
...
def main():
+ parser = argparse.ArgumentParser(description='Convert URDF to MuJoCo XML model.')
+ parser.add_argument('input_urdf', help='Path to the input URDF file')
+ args = parser.parse_args()
+
+ input_urdf = args.input_urdf
- if not os.path.exists(INPUT_URDF):
+ if not os.path.exists(input_urdf):
print('Error: URDF file not found:', input_urdf)
sys.exit(1)
- urdf_tree = etree.parse(INPUT_URDF)
+ urdf_tree = etree.parse(input_urdf)Committable suggestion was skipped due to low confidence.
| # Add actuators to the top level | ||
| actuator = etree.Element('actuator') | ||
| for joint in tree.findall('.//worldbody/.//joint'): | ||
| actuator.append(etree.Element('position', **{'joint': joint.attrib['name'], 'name': joint.attrib['name'], 'class': ACTUATOR_MAPPING[joint.attrib['name']] })) |
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Ensure joint names exist in ACTUATOR_MAPPING when creating actuators.
Similar to the previous issue, when adding actuators, ensure that the joint names are present in ACTUATOR_MAPPING to prevent errors.
Suggested code change:
for joint in tree.findall('.//worldbody/.//joint'):
+ actuator_class = ACTUATOR_MAPPING.get(joint.attrib['name'])
+ if actuator_class:
actuator.append(etree.Element('position', **{
'joint': joint.attrib['name'],
'name': joint.attrib['name'],
- 'class': ACTUATOR_MAPPING[joint.attrib['name']]
+ 'class': actuator_class
}))
+ else:
+ print(f"Warning: Joint '{joint.attrib['name']}' not in ACTUATOR_MAPPING.")Committable suggestion was skipped due to low confidence.
| WalkNode::WalkNode(rclcpp::Node::SharedPtr node, const std::string& ns, | ||
| std::vector<rclcpp::Parameter> moveit_parameters) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Pass 'moveit_parameters' by const reference to avoid unnecessary copies
Currently, the constructor accepts std::vector<rclcpp::Parameter> moveit_parameters by value. To prevent unnecessary copying of potentially large vectors, consider passing it by const reference instead:
WalkNode::WalkNode(rclcpp::Node::SharedPtr node, const std::string& ns, const std::vector<rclcpp::Parameter>& moveit_parameters)
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Add support for the MuJoCo Simulator
This is still very much WIP.