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Teaching Robots to Walk: Our First Steps into Humanoid AI

Part 1 of our Humanoid Building Journey

The Dream of Walking Machines

Building a humanoid robot that can walk naturally has been a dream of roboticists for decades. While we've seen impressive demonstrations from companies like Boston Dynamics and Tesla, the algorithms and training methods behind these achievements have largely remained proprietary black boxes.

That's why we were excited to discover K-Scale Labs' KBot and their open-source KSimGym framework – a complete, production-ready system for training humanoid locomotion using modern reinforcement learning techniques.

Why Start with Simulation?

Before diving into expensive hardware, we knew we needed to understand the fundamentals of humanoid control. Real robots are costly, fragile, and time-consuming to iterate on. Simulation allows us to:

  • Fail fast and cheap: No broken servos or damaged components
  • Parallelize learning: Run thousands of virtual robots simultaneously
  • Perfect the algorithms: Get the AI right before moving to hardware
  • Understand the science: Learn what makes humanoid walking work

Discovering KSimGym: A Production-Ready Framework

K-Scale Labs has open-sourced something remarkable with KSimGym – a complete reinforcement learning pipeline specifically designed for humanoid locomotion. After studying their codebase, we were impressed by the sophistication of their approach:

The Neural Architecture

Their system uses a actor-critic approach:

  • Actor Network: A recurrent neural network that learns to control the robot's 20 joints
  • Critic Network: A value estimator that judges how "good" each state is

What's clever is the asymmetric design – the actor gets minimal observations for fast real-time control, while the critic analyzes rich state information for accurate learning.

Smart Action Representation

Instead of simple joint commands, their actor outputs a "mixture of Gaussians" for each joint. This means the robot can represent multiple possible actions with different confidence levels – similar to how humans have multiple ways to take a step.

Robust Training Philosophy

The framework includes extensive domain randomization:

  • Random physics parameters (friction, mass, joint stiffness)
  • Sensor noise that mimics real hardware
  • Action delays and dropouts
  • External disturbances (random pushes)

This isn't just academic – it's designed to transfer to real hardware.

The Learning Process

Watching the training process is fascinating. The robot starts with completely random movements, gradually learning through trial and error:

  1. Survival Phase: First learns to avoid falling over
  2. Movement Phase: Discovers how to move forward
  3. Optimization Phase: Refines gait for efficiency and stability
  4. Robustness Phase: Handles disturbances and variations

The reward system elegantly balances multiple objectives:

  • Forward progress (main goal)
  • Staying upright (stability)
  • Energy efficiency (smooth movements)
  • Natural posture (human-like appearance)

What We've Learned So Far

Diving into KSimGym has been eye-opening. Here are our key takeaways:

Walking is Incredibly Complex

Even with 20 degrees of freedom, creating natural humanoid locomotion requires balancing hundreds of competing factors. The robot must coordinate:

  • Dynamic balance
  • Forward momentum
  • Joint limits
  • Energy efficiency
  • Robustness to disturbances

Modern AI Makes It Possible

Reinforcement learning, particularly PPO (Proximal Policy Optimization), provides a principled way to solve this multi-objective optimization problem. The robot literally learns to walk the same way humans do – through practice and feedback.

Simulation Quality Matters

High-fidelity physics simulation (MuJoCo) is crucial. The robot must learn behaviors that will transfer to the real world, which requires realistic modeling of:

  • Joint dynamics
  • Contact forces
  • Sensor noise
  • Actuator limits

Engineering Excellence

KSimGym isn't just a research demo – it's production-ready code with:

  • Massive parallelization (2048 environments)
  • Comprehensive logging and monitoring
  • Professional hyperparameter tuning
  • Robust checkpointing and recovery

Our Next Steps

This exploration of KSimGym has given us the foundation we need to start our own humanoid journey. We're now confident in:

  • The fundamental RL algorithms for humanoid control
  • The importance of simulation-to-real transfer
  • The engineering requirements for production systems

Why This Matters

Open-source frameworks like KSimGym are democratizing robotics. What used to require massive corporate R&D budgets can now be explored by anyone with curiosity and computational resources.

We're standing at the threshold of an era where humanoid robots will become as common as smartphones. By understanding and building upon these foundational technologies, we're not just observers – we're participants in creating that future.

Follow along as we document our journey from simulation to real walking humanoids. The future of humanoid AI isn't just in corporate labs – it's in the hands of builders, makers, and dreamers willing to take the first step.

Coming Next: we'll see