I’m working with the humanoid_runner environment and I need to customize the physical characteristics of the agent. Specifically, I want to adjust things like the arm span or torso dimensions.
I’ve been looking through the documentation but I can’t find clear instructions on how to modify these structural parameters. The default agent setup works fine, but my research requires testing with different body proportions.
Has anyone successfully altered the agent’s physical attributes in OpenAI Gym? I’m wondering if there’s a built-in way to do this or if I need to dive into the source code and create a custom version of the environment.
Any guidance on the best approach would be really helpful. I’m particularly interested in whether these changes can be made without breaking the existing reward structure.
I’ve found the cleanest approach is building a config system that maps body parameters to MuJoCo model attributes. Direct XML manipulation gets messy fast when you’re doing morphology research and need reproducible experiments.
What works way better: create a parameter mapping layer. Define your physical traits (arm span, torso size) as high-level parameters, then convert these to MuJoCo geom and body elements through code. You get version control and parameter sweeps become much easier.
For reward structure - I’ve tested this tons with humanoid locomotion tasks. Most reward functions focus on relative motion and stability metrics, not absolute body measurements. Your modified agents will find different optimal policies, but the reward signal stays meaningful.
One thing that bit me: when you scale body parts, adjust the joint damping and friction parameters too. Default values are tuned for standard proportions. Extreme modifications can create unstable dynamics that mess with your locomotion task.
The XML approach works, but here’s another option. I subclassed the base environment and overrode the model generation step completely. This way you can pass body dimensions as constructor arguments instead of juggling separate XML files. When I was doing gait analysis research, I built a wrapper that takes scaling factors for different body segments. The trick is modifying the mjModel after loading but before the first reset call. This keeps your parameter space searchable and makes it super easy to sample random body configs during training. For rewards, humanoid_runner’s reward function handles body proportion changes pretty well. It mainly tracks forward progress and penalizes falling, which works across different morphologies. Just watch out for extreme aspect ratios - they can make the default joint limits unrealistic. You’ll probably need to scale torque limits proportionally to keep similar movement capabilities.
You can definitely modify physical properties in Gym environments, but manually editing XML files and recompiling sucks when you’re running tons of experiments.
I’ve been there - we needed to test different agent configs for robustness studies. Best solution? Set up an automated pipeline that generates modified environments on demand.
Parameterize your modifications so you can batch process different body proportions without tweaking each one manually. Automate the XML generation, environment registration, and run comparison tests across multiple configs at once.
This saved us weeks when we had to test 50+ different body configurations. Instead of wasting time on repetitive stuff, we could actually analyze results.
For reward structures, automation helps here too. You can validate that your modifications don’t break existing functionality before running full experiments.
Latenode handles this workflow automation really well, especially coordinating between environment generation, testing, and results collection.
Those physics parameters are buried in the MuJoCo XML files that define the humanoid model. Find the XML file for humanoid_runner (usually in the assets directory) and modify the geom elements that control body segment dimensions.
I hit similar issues when adapting humanoid environments for biomechanics research. Here’s the thing - changing limb lengths messes with mass distribution and joint limits. You can’t just scale dimensions randomly. You’ve got to recalculate inertial properties and probably adjust actuator ranges to keep movement patterns realistic.
For reward structure, most humanoid tasks use velocity or distance-based rewards that work fine regardless of body proportions. But you’ll probably see performance differences since longer limbs change optimal gait patterns.
Make a custom environment class that inherits from the base humanoid environment. Override the model loading to use your modified XML. This keeps your changes separate and makes it way easier to experiment with different configs without breaking the original.
Honestly, just copy the entire humanoid XML file and create a separate environment class. It’s not elegant, but it’s fast when you’re testing different body configs. Register your custom env with gym and you’re set. I’ve used this approach for 6 different arm/torso variations - never had reward issues.
The Problem: You’re trying to modify the physical characteristics (arm span, torso dimensions) of the humanoid agent in the humanoid_runner environment within OpenAI Gym, but you’re unsure how to do this without disrupting the reward structure. Manually editing XML files is tedious and error-prone when conducting numerous experiments with varying body proportions.
Understanding the “Why” (The Root Cause):
The core issue is the inefficiency of manually altering MuJoCo XML files for each experiment. This approach is slow, prone to errors (easily leading to inconsistencies between experiments), and doesn’t scale well for large-scale morphology research involving many different body configurations. Automating this process is crucial for efficiency and reproducibility. Direct XML manipulation lacks version control and makes parameter sweeps extremely challenging.
Step-by-Step Guide:
Automate Environment Generation: Instead of manually modifying XML files, create a Python script that programmatically generates modified humanoid_runner environments based on your desired parameters (arm span, torso dimensions, etc.). This script should handle the creation of the MuJoCo XML, the registration of the new environment with OpenAI Gym, and potentially the creation of necessary assets.
import mujoco
# ... other imports ...
def generate_modified_environment(arm_span, torso_size, ...):
# Load the base XML model
xml_string = load_xml_file("humanoid_runner.xml") # Replace with your actual file path
# Modify the XML using mujoco.MjcfElement
model = mujoco.MjcfElement.from_xml_string(xml_string)
model.find('geom', 'torso').size = [torso_size, ..., ...] # Example: adjust torso size
model.find('geom', 'arm').size = [arm_span, ..., ...] # Example: Adjust arm size. Adapt selectors!
# ...modify other relevant parameters...
# Save the modified XML. Make sure the naming convention helps distinguish the files
modified_xml_string = model.to_xml_string()
save_xml_file(modified_xml_string, f"humanoid_runner_{arm_span}_{torso_size}.xml")
# Register the modified environment with Gym (this will require additional code)
# ... registration code ...
return modified_environment # Return the newly registered environment
Parameterize Your Experiments: Define a set of parameters that represent the range of body proportions you want to test. This will allow you to easily iterate through different configurations. Store these parameters in a structured format (e.g., a CSV file or a Python dictionary).
Batch Run Your Experiments: Use a process manager (e.g., multiprocessing in Python) to run training sessions for each of the generated environments concurrently. This massively speeds up the process.
Collect and Analyze Results: Implement a system to automatically collect relevant metrics (e.g., reward scores, episode lengths) from each training session. Then, use data analysis tools (e.g., Pandas, Matplotlib) to visualize and compare the results across different body configurations.
Verify Reward Structure: Before running extensive experiments, conduct preliminary tests to ensure that the modified reward structure still behaves as expected. Small adjustments to reward functions may be needed depending on the changes in the agent’s morphology.
Common Pitfalls & What to Check Next:
Joint Limits and Actuator Ranges: Significant changes to body proportions might necessitate adjustments to joint limits and actuator ranges to prevent unrealistic or unstable movements. Pay close attention to these values in your MuJoCo XML.
Inertia Properties: Recalculating inertial properties after modifying body segment dimensions is crucial for realistic physics simulation. MuJoCo may have utilities to help with this.
Error Handling: Implement robust error handling in your automation script to gracefully manage potential issues during environment generation and training. This includes checks for file existence, XML parsing errors, and Gym environment registration failures.
Debugging your XML: XML parsing errors are a common pitfall when working with XML files. Using an XML validator can save considerable time.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
I’ve been working with humanoid environments and found it’s way better to modify MuJoCo models through Python rather than manually editing XML files. You get much more control.
Here’s what works: after you initialize the environment, grab the model object and use the mjcf module to adjust body dimensions programmatically. The trick is that OpenAI Gym’s MuJoCo environments expose the underlying model through the sim attribute. Just clone the base environment, tweak the body parameters you need (geom sizes, joint positions, whatever), then rebuild the simulation. Way cleaner for version control than having XML files scattered everywhere.
For rewards - I’ve tested this a ton and most humanoid locomotion rewards stay stable if you keep reasonable proportions. The environment usually tracks forward velocity and stability metrics, which scale naturally when you change body size. But definitely run baseline tests with your modified proportions before you start your main experiments, just to be safe.
Understanding the “Why” (The Root Cause):
Step-by-Step Guide:
Common Pitfalls & What to Check Next:
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!