How to display and output contact force and joint information in the process or results of reinforcement learning？

[TomJimmy-33]

There is limited information available for data visualization. I need to output the joint information and contact information between the foot and the ground from the reinforcement learning training results of the humanoid robot. How can I add relevant sensors during the training process?

[RandomOakForest]

Thank you for posting this. I'll move your question to our Discussions section for follow up. To start, here are some implementation strategies and notes to consider:

1. Joint State Monitoring

Use the robot's articulation API to access joint data without additional sensors:

# Get joint positions and velocities
joint_pos = scene["robot"].data.joint_pos
joint_vel = scene["robot"].data.joint_vel

# Get measured joint forces (e.g., motor torques)
joint_efforts = scene["robot"].data.measured_joint_efforts

These properties update automatically during simulation¹.

2. Foot-Ground Contact Sensing

Add contact sensors to the feet using ContactSensorCfg:

from isaaclab.sensors import ContactSensorCfg

contact_sensor_cfg = ContactSensorCfg(
    prim_path="{ENV_REGEX_NS}/Robot/.*_FOOT",  # Regex for foot links
    update_period=0.0,  # Update every physics step
    debug_vis=True,      # Visualize contacts in simulator
    history_length=1     # Store latest contact data
)

Key parameters:

• prim_path: Regex pattern matching foot links (e.g., .*_FOOT for names like LEFT_FOOT/RIGHT_FOOT)
• update_period=0.0: Ensures real-time contact detection²³
• mesh_prim_paths=["/World/defaultGroundPlane"]: Filters contacts only with ground³

3. Data Extraction During Training

Access contact forces in the RL step function:

# Net contact force (world frame) [^3]
foot_contacts = scene["contact_sensor"].data.net_forces_w

# Example output shape: [num_envs, num_feet, 3]
# For humanoid: 2 feet × 3D force vectors

4. Visualization/Logging

Implement data logging using Isaac Lab's DataLogger:

def log_frame_data():
    return {
        "joint_positions": joint_pos.tolist(),
        "joint_efforts": joint_efforts.tolist(),
        "foot_contacts": foot_contacts.tolist()
    }

# Add to data logger
world.get_data_logger().add_data_frame_logging_func(log_frame_data)
world.get_data_logger().start()  # Begin recording

Save data periodically using world.get_data_logger().save(log_path)⁴.

Key Configuration Notes:

• Enable contact reporting: Ensure enable_contact_reporting=True in the robot's asset configuration³.
• Sensor placement: Attach sensors to rigid bodies (feet) via prim_path.
• Physics filtering: Use mesh_prim_paths to isolate foot-ground contacts³⁵.

Footnotes

1. https://docs.omniverse.nvidia.com/isaacsim/latest/features/sensors_simulation/isaac_sim_sensors_physics_articulation_force.html ↩

2. https://isaac-sim.github.io/IsaacLab/main/source/tutorials/04_sensors/add_sensors_on_robot.html ↩

3. https://isaac-sim.github.io/IsaacLab/main/source/overview/core-concepts/sensors/contact_sensor.html ↩ ↩² ↩³ ↩⁴

4. https://docs.isaacsim.omniverse.nvidia.com/4.5.0/core_api_tutorials/tutorial_advanced_data_logging.html ↩

5. https://docs.isaacsim.omniverse.nvidia.com/4.5.0/sensors/isaacsim_sensors_physics_contact.html ↩