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Humanoid Locomotion

Humanoid Locomotion is the study and implementation of walking, running, and balance control in humanoid robots. It combines kinematics, dynamics, sensor feedback, and control algorithms to enable robots to move safely and efficiently in complex environments.

This lesson introduces the fundamentals, control strategies, and ROS 2 integration for humanoid locomotion.

Learning Objectives​

By the end of this lesson, students will be able to:

  • Understand the mechanics of humanoid locomotion
  • Describe bipedal walking patterns and gait cycles
  • Apply inverse kinematics and dynamics for humanoid movement
  • Use sensors for balance and stability
  • Implement locomotion control using ROS 2 Humble
  • Simulate humanoid walking in Gazebo, Unity, or Isaac Sim
  • Analyze trade-offs in humanoid locomotion design

Prerequisites​

  • Completed Week 1-8: ROS 2, simulation, and sensor integration
  • Completed Week 2: Humanoid Fundamentals (DOF, actuators)
  • Understanding of kinematics and dynamics principles
  • Familiarity with control theory and feedback systems
  • ROS 2 Humble with MoveIt or similar motion planning library
  • Access to simulation platform (Gazebo, Unity, or Isaac Sim)
  • Python 3.8+ and NumPy for kinematic calculations

1. Introduction to Humanoid Locomotion​

Humanoid locomotion involves:

  • Bipedal walking: Alternating leg motion for forward movement
  • Running: Faster motion with flight phase
  • Turning & pivoting: Changing direction smoothly
  • Balance maintenance: Preventing falls

✅ Challenges include stability, energy efficiency, and terrain adaptation.

2. Gait Cycle​

A gait cycle is the sequence of movements during walking:

  1. Stance phase: Foot in contact with ground
  2. Swing phase: Foot moves forward
  3. Double support phase: Both feet in contact

✅ Understanding gait cycles is essential for stable humanoid walking.

🔹 3. Kinematics and Dynamics​

Forward Kinematics:​

  • Compute end-effector (foot) position from joint angles

Inverse Kinematics:​

  • Compute joint angles to achieve desired foot position

Dynamics:​

  • Compute forces, torques, and motion stability
  • Consider gravity, momentum, and ground reaction forces

✅ Combined kinematics & dynamics are essential for walking and balance.

👣 4. Balance and Stability​

Key Concepts:​

  • Center of Mass (CoM)
  • Zero Moment Point (ZMP): Point where robot does not tip
  • Foot placement strategies

Sensors Used:​

  • IMU (Inertial Measurement Unit)
  • Force sensors in feet
  • Joint encoders

✅ Continuous feedback from sensors allows real-time correction.

5. Humanoid Locomotion Control Strategies​

1. Open-Loop Control​

  • Predefined joint trajectories
  • No feedback from sensors
  • Simple but less robust

2. Closed-Loop Control​

  • Uses sensor feedback (IMU, force sensors)
  • Adjusts motion in real-time
  • More stable and adaptable

3. Model Predictive Control (MPC)​

  • Predicts future states using robot model
  • Optimizes stability and energy
  • Common in advanced humanoid robots

6. ROS 2 Integration​

Humanoid locomotion can be implemented in ROS 2 using:

  • Joint state publishers → Control leg joints
  • IMU and foot sensors → Feedback for balance
  • Motion planning nodes → Generate walking trajectories
  • Gazebo/Unity/Isaac Sim → Simulate locomotion

✅ Enables testing and tuning locomotion safely in simulation.

7. Practical Examples​

Example 1: Forward Walking​

  • Publish joint angles via ROS 2
  • Use IMU feedback to correct balance
  • Simulate walking in Gazebo

Example 2: Turning​

  • Adjust foot trajectory
  • Maintain CoM stability
  • Execute turn smoothly

Example 3: Obstacle Negotiation​

  • Use vision/LiDAR to detect obstacles
  • Adjust gait dynamically
  • Maintain stability

8. Humanoid Locomotion in AI & Robotics​

  • Integration with AI planners for dynamic environments
  • Training reinforcement learning agents for walking
  • Combine with VLA models for perception-driven navigation
  • Enables humanoids to perform complex tasks safely

🧪 9. Hands-On Exercises (Coming Soon)​

✅ Simulate a simple biped robot in Gazebo
✅ Implement forward walking using ROS 2 joint commands
✅ Integrate IMU for balance feedback
✅ Test turning and pivoting
✅ Simulate walking over uneven terrain
✅ Experiment with open-loop vs closed-loop control

10. Knowledge Check Quiz (Coming Soon)​

  • What is a gait cycle?
  • Difference between open-loop and closed-loop control?
  • Role of ZMP in humanoid locomotion?
  • Sensors commonly used for balance?

11. Glossary​

  • Gait Cycle: Sequence of movements during walking
  • CoM: Center of Mass
  • ZMP: Zero Moment Point
  • IMU: Inertial Measurement Unit
  • MPC: Model Predictive Control
  • Joint Encoder: Measures joint angles

12. Further Reading (Coming Soon)​

  • Humanoid locomotion research papers
  • ROS 2 locomotion tutorials
  • Gazebo/Unity/Isaac Sim humanoid walking examples
  • Reinforcement learning for biped robots
  • Balance and stability control techniques

Lesson Summary​

This lesson introduced humanoid locomotion, including gait cycles, kinematics & dynamics, balance control, and ROS 2-based locomotion strategies. Students learned how sensors, control algorithms, and simulations enable humanoid robots to walk, turn, and maintain stability safely.

📌 This lesson prepares students for advanced humanoid robotics, AI-driven locomotion, and simulation-based testing using ROS 2.

Version: ROS 2 Humble
License: CC BY-SA 4.0