## Dynamic movement primitives part 3: Rhythmic movements

So far we’ve looked at using DMPs for discrete movements, but as I briefly mentioned it’s also possible to use them to perform rhythmic movements. In this post we’re going to look at the implementation of rhythmic DMPs and see exactly how it’s done. It’s actually pretty straightforward, which is always nich. We’ll go through everything that has to change and then see a couple of different applications of rhythmic DMPs. Again, all the code for the DMPs and arm control implementations can be found up on my Github.

Obtaining consistent and repeatable basis function activation

We’ll start with the canonical system. In the discrete case our canonical system (which drives the activation of the basis functions) decayed from $1$ to $0$ and then we were all done. In the rhythmic case we want our system to repeat indefinitely, so we need a reliable way of continuously activating the basis functions in the same order. One function that may come to mind that reliably repeats is the cosine function. But how to use the cosine function, exactly?

The idea is that we’ll lay out our basis functions in the range from $0$ to $2\pi$, and we’ll set the canonical system to be ever increasing linearly. Then we’ll use the difference between the canonical system state and each of the center points as the value we pass in to our cosine function. Because the cosine function repeats at $2\pi$ we’ll get a nice repeating spread of basis function activations. We’ll then throw these values through our Gaussian equation (with the gain and bias terms) and we’ll be done! Here’s the equation:

$\psi = \textrm{exp}(h * \textrm{cos}(x - c) - 1),$
where $x$ is the state of the canonical system, $c$ is the basis function center point, and $h$ is a gain term controlling the variance of the Gaussian. And here’s a picture of the activations of three basis functions with centers spread out evenly between $0$ and $2\pi$:

Here’s a picture of the basis function activations on a longer time scale:

As long as our canonical system increases at a steady pace the basis functions will continue to activate in a reliable and repeatable way. As mentioned, to get the canonical system to increase at a steady pace we simply give it linear dynamics:

$\dot{x} = 1$
The placement of the center points of the rhythmic system is a lot easier than in the discrete case because the rhythmic canonical system dynamics are linear.

Other differences between discrete and rhythmic

In the actual implementation of the rhythmic system there are a couple of other differences that you need to be aware of. The first is that there is no diminishing term in the rhythmic system, whereas there is in the discrete case.

The second is how to go about establishing goal states when imitating an path. We’re going to assume that whatever path is passed in to us is going to be a rhythmic pattern, and then goal state is going to be set as the center point of the desired trajectory. So for rhythmic systems the ‘goal’ is actually more like a center point for the trajectory.

And that’s pretty much it! If you look through the code that I have up for the rhythmic DMP subclass you’ll see that only a handful of functions needed to be redefined, the rest is the same! Well that’s great, let’s look at some applications.

Example

I was thinking of different examples that would be interesting to see of the rhythmic DMPs, and after a little consideration the most obvious application of rhythmic DMPs is really to a system that does something like walking. In the spinal cord of animals there are circuits which are known as central pattern generators (CPGs), and when stimulated these have been shown to generate rhythmic, repeated movements (as described in Principles of rhythmic motor pattern generation and other papers). Let’s go ahead and build our own CPG here using DMPs. I don’t have a simulation of a set of legs, however, so instead we’ll look at getting a single 3-link arm to reproduce a pattern of behaviour that you’d expect in a leg that is walking.

To do this, I’m going to need to specify the trajectories for the three joints of the arm, and I’ll be controlling the system in joint space, using the generalized coordinates controller, rather than the operational space controller used in the discrete DMP examples for controlling the end-effector position. So that’s one thing. Another thing is that I’m going to need the kinematics of leg joints during motion. After a quick search of the web I found this presentation from Simon Fraser University which has the following images:

Which is some very useful information! I just need to transfer this into a Numpy vector that I can feed into the system and convert degrees to radians and we should be good to go! To get the information out of the graph, use your favorite data stripper, I used http://arohatgi.info/WebPlotDigitizer/app/.

I also had to do a bit of a shuffle with the trajectories provided to make it look right on the arm. The hip to shoulder joint maps fine, but for the knee to elbow the flexion and extension are reversed, and for the wrist to foot the $0$ angle for the foot is straight out. Once those have been accounted for, though, everything works great. Here is an animation of a single leg walking along, if you use your imagination you can just picture a happy-go-lucky torso attached to this leg whistling while he gets his very human-like gait on:

And there you go! It’s neat how straightforward that was, once you have the data of the system you want to model. It shows the power of DMPs for coordinating multiple degrees of freedom quickly and easily. Also something that’s worth pointing out once again is that this is a force based controller. We were able to go from a kinematic description of the movement we wanted to a reliable system generating torques. It’s another good example of the dexterity of dynamic movement primitives.

You can see the code for everything here up online on my github control repo on the walking_demo branch. To run the code you should run:

python run.py