How Robots Learned to Walk: From Hand-Engineered Control to Reinforcement Learning
Episode von RobTalk von RobCo · 27. August 2026 · 43 min
Is legged locomotion actually a solved problem?
In this episode of RobTalk, Felix Frank from our Robot Intelligence team explains how legged robots learn to walk, and why going from an impressive stage demo to a reliable real-world deployment is still one
of the hardest open problems in robotics.
You'll gain insights into:
- Why footstep planning used to mean months of hand-engineered optimization
- How GPU-parallelized simulation and domain randomization changed the entire approach
- What retargeting means, and why human motion data now trains robot policies
- The difference between imitation learning and adversarial motion priors
- Why legged robots face real safety and power challenges that fixed robots don't
- What is still unsolved: combining blind whole-body control with real terrain understanding
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
01:14 – Rob Talk intro & welcoming Felix Frank
01:49 – Felix's background
02:38 – Breakout projects at VW (e.g., compressed air control)
03:45 – Move into humanoid robotics (US startup, whole-body control)
04:23 – The classical engineering approach: footstep planning & online optimization
06:33 – Sensor fusion: IMUs, contact sensors & Kalman filtering
08:39 – What is a kinematic tree?
10:08 – Limits of the classical approach (door opening, manipulation)
12:19 – The optimization problem: cost functions & constraints
14:40 – Boston Dynamics' Atlas & the limits of hand-engineering
17:18 – The paradigm shift: GPU-parallel simulation & the Unitree G1
18:11 – Reinforcement learning explained: reward functions & domain randomization
23:50 – Domain randomization in depth
25:26 – Building robustness through external perturbations in training
26:23 – Motion imitation: mocap, retargeting & DeepMimic (2018)
30:57 – The data-centric approach: large-scale datasets & NVIDIA Sonic
33:39 – Why the humanoid form makes sense (locomotion vs. manipulation)
34:54 – Blind locomotion: how far can you get without perception?
36:35 – Terrain awareness & planner components
39:20 – Legged vs. wheeled robots: safety & fail-safe behavior
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