Biomimetic Synthetic Gait – First peek at my Bachelor Thesis

Here is the first sneak peek of my Bachelor Thesis for Mechanical Engineering. It is wildly optimistic, super amazing and touches upon topics like: mechanics, software, neuroscience, robot technology, AI, control theory, physiology, biomechanics, genetic algorithms, underactuated dynamical systems as well as muscle redundancy problems.

The elevator speech is still an elusive one, but here goes:

My overall objective is to gain insight into the human nervous system related to gait koordination. How are the muscles orchestrated, which areas handle what, what is the actual involvement of the brain, what has been subcontracted elsewhere and how could a theoretical model of this look like.

My approach is through using a so called Predictive Controller for Forward Dynamics simulation. I am mainly concerned with humanoid gait patterns and have ended up basing my project on an existing system called PredictiveSim.

This system is written by and published alongside the following article:

Dorn, T. W., Wang, J. M., Hicks, J. L., & Delp, S. L. (2015). Predictive Simulation Generates Human Adaptations during Loaded and Inclined Walking. Plos One, 10(4), e0121407. doi:10.1371/journal.pone.0121407

In short terms the system defines a humanoid model with bones, joints, tendons and muscles along with a number of reflex loops between muscles and senses (Ie: foot contact, muscle stretch etc.). Basically just a greatly simplified model of a human containing only what is believed to be the most neccessary parts related to walking – Weirdly enough the brain isn’t included.

With this model you can define a goal that it should aim for, ie: try to move forward with a certain speed while doing your best to minimize energy consumption. This is a problem that gets solved/optimized by a Genetic Algorithm and after leaving it in the hands of a computer (or several) a lot of thinking starts happening but out comes something similar to this:

It doesn’t look like much for an animator or in general, but the interesting bit is that nobody has told this model how to move. It never saw anyone doing it, there are no parameters in the model that dictates how this should be done, no keyframes or experience or anything. The only input it got was to try to move and do it in the most energyefficient way.
Realizing that this looks somewhat like human gait in general, one may get the feeling that minimizing energy consumption is a pretty big reason for why we move as we do.

If anyone feels like trying it out for themselves feel free to either get the original code or pick up my version, which I will be updating throughout my project. So far it mainly has a bit more documentation and annotation.
It can be found here: jwPredictiveSim at bitbucket.org

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