Getting Started with Humanoid Robotics

Why Humanoid Robots?

I have always been fascinated by the idea of machines that move and interact like humans. When Boston Dynamics released their Atlas videos, I knew I wanted to understand what makes bipedal locomotion work. This post documents my first week diving into the fundamentals.

The Basics of Bipedal Locomotion

Humanoid robots present a unique challenge compared to wheeled robots. They need to:

• Maintain dynamic balance while walking
• Plan footstep trajectories in real-time
• Handle uneven terrain and unexpected disturbances
• Coordinate dozens of actuators simultaneously

The core concept is the Zero Moment Point (ZMP), which determines whether the robot will remain stable during movement. If the ZMP stays within the support polygon (the area under the feet), the robot stays upright.

Setting Up a Python Development Environment

I started with a clean Python environment using venv and installed the essentials:

# Create and activate virtual environment
python -m venv robotics-env
source robotics-env/bin/activate

# Install core dependencies
pip install numpy scipy matplotlib
pip install roboticstoolbox-python
pip install swift-sim

The roboticstoolbox-python library by Peter Corke is an incredible resource. It provides implementations of common robotics algorithms and a simulator to visualize robot motion.

My First Inverse Kinematics Solver

Inverse kinematics (IK) is the problem of figuring out what joint angles produce a desired end-effector position. For a humanoid robot, this means calculating how each joint in the leg should bend to place the foot at a specific location.

import roboticstoolbox as rtb
import numpy as np

# Create a simple 2-link planar robot (like a simplified leg)
robot = rtb.models.DH.Planar2()

# Desired end-effector position
target = rtb.SE3(0.3, 0.4, 0)

# Solve inverse kinematics
solution = robot.ikine_LM(target)
print(f"Joint angles: {np.degrees(solution.q)}")

This is a massively simplified example, but it captures the essence. Real humanoid robots have 30+ degrees of freedom, making the IK problem significantly more complex.

What I Learned This Week

The gap between understanding robotics theory and making a physical robot move is enormous, but Python makes the journey approachable.

Key takeaways from my first week:

• Start with simulation. Do not buy hardware until you can make a simulated robot walk.
• Linear algebra matters. Rotation matrices and homogeneous transformations are everywhere in robotics.
• ROS2 is the standard. Most serious robotics work uses the Robot Operating System, and Python bindings make it accessible.

Next week, I plan to set up a ROS2 workspace and try controlling a simulated humanoid in Gazebo.