Free Download Humanoid Robotics 2026 Balance, Walking & RL in MuJoCoPublished 8/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle |
Duration: 8h 51m |
Size: 6.57 GB
Build humanoid balance and walking control for a Unitree H1-2 in MuJoCo: torque control, ZMP, push recovery and RL.
Build a balance controller for a 67 kg Unitree H1-2 humanoid in MuJoCo, from torque control and gravity compensation to a measured ankle strategy.
Estimate centre of mass and centre of pressure from model and sensor data, and use them to keep a humanoid standing against a measured push disturbance.
Make a humanoid walk: LIPM trajectories, foot placement, swing-leg inverse kinematics and a whole-body QP that turns those trajectories into joint torques.
Run and read a reinforcement learning locomotion policy, compare it against your classical stack, and take both to ROS 2 and Gazebo in a full capstone.
RequirementsPython and basic linear algebra. No robot and no prior humanoid experience needed: everything runs in MuJoCo on a normal laptop, and the robot model is free.
DescriptionThis course contains the use of artificial intelligence.
A humanoid robot has a support polygon the size of a shoe. That is the whole problem.
A wheeled robot can stop and stand still. A humanoid standing still is running a controller, and if that controller stops, it falls.
This course builds that controller from the ground up using the
Unitree H1-2 humanoid in
MuJoCo, entirely in simulation. You do not need physical hardware, and this is not a slide deck about robots someone else built.
We start by breaking it.
Hold every joint at its exact angle, let go, and the robot collapses from
1.03 m to 0.415 m in six seconds.
That measured failure drives everything that follows: why position control cannot balance a humanoid, what torque control actually gives you, and why gravity compensation alone does not solve the problem.
What You Will Build
You will progressively build the humanoid balancing and locomotion stack
- Estimate
center of mass from the robot model and state
- Estimate
center of pressure from ankle force/torque sensors
- Derive and implement the
ankle strategy, then push-test it to find where it fails
- Build an
upper-body momentum strategy suited to the H1-2
- Implement
capture-point stepping and determine reachable foot placements
- Generate walking trajectories using the
Linear Inverted Pendulum Model- Solve
swing-leg inverse kinematics- Build a
whole-body quadratic program that converts motion objectives into joint torques
- Run Unitree's
pre-trained reinforcement-learning locomotion policy- Break down its
47-dimensional observation vector term by term
- Compare a learned locomotion policy against the classical controller you built yourself
- Extend the system to
terrain, arms, and hands- Integrate the system with
ROS 2 and Gazebo- Finish with a capstone where the humanoid
walks, balances, and carriesLearn From What Fails
Every number shown in the course comes from an experiment.
When something does not work, we study why instead of hiding it. That includes gravity compensation that made the robot perform worse and a textbook hip strategy that this particular humanoid cannot execute in the conventional way.
You receive the scripts used to produce the experiments and figures, so you can rerun them, modify them, and test the results yourself.
What You Need
Required: Python, basic linear algebra, and a laptop.
Not required: a humanoid robot, ROS experience, or previous humanoid robotics experience.
Who this course is forRobotics engineers and ML practitioners who can code in Python and want humanoid balance and locomotion built from measured results, not slide-deck theory.
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