I'm Stéphane, a roboticist who enjoys teaching things to balance and walk. I have developed locomotion software for HRP-4 humanoids, ANYmal quadrupeds, and more recently Tast's Robots like Upkie. You can read the algorithms in the corresponding publications or grab the code on GitHub. Occasionally, I tweet and post technical notes to this website. Don't hesite to send me an e-mail, answer guaranteed if you use my GPG key :-)
I've been wondering why we don't have more walking machines around us already. One explanation is that there is a scalability ceiling in implementing locomotion by control systems. For example, in the walking controller that makes the HRP-4 humanoid climb stairs, the most critical part for reliability is the robot's stabilizer, which decides how to keep balance in reaction to disturbances: roll the ankle? Shift the pelvis? Maybe tilt the torso as well? On HRP-4, all these strategies were implemented by high-frequency control systems such as linear feedback (with gain parameters) and model predictive controllers (with cost parameters), whose tuning is challenging to scale to new behaviors.
I search for methods and sensory representations that will allow us to go beyond the parameter-tuning ceiling, especially for robots that are flexible and have slow brains. On the way I built Upkie, a wheeled biped that proudly stands on sawed broomsticks (among other mechanical marvels), is fully makeable at home and moves around with open source locomotion software. Upkie is helpful to try all kinds of things. Its Python brain runs at a slow pace on a Raspberry Pi, which is OK because valuable tasks like balancing can be performed at remarkably low frequencies.
I'm a strong supporter of open access and was fortunate to be able to release my research works as pre-prints and source code for all but a few exceptions. On top of open access, I see three ways in which the robotics community can improve itself: overlay journals, post-prints, and raising our standards in terms of code distribution.