The trials and tribulations of training a physical robot with reinforcement learning

This is a follow up to my article “Training a real robot to play Puckworld with reinforcement learning”. In that one, to make it a little punchier, I showed the overview and end results of the project, but left out the insane number of little hurdles and decisions I had to figure out.

So this article will be about those details instead, partly for me to justify the pain, but maybe more charitably to show that for any project with a neat (hopefully?) presentation, there’s probably a harrowing saga of hair-pulling roadblocks behind it. Here, it’s roughly in the order I encountered things. There are lots I’m leaving out too. read more

The Mostly Printed CNC (MPCNC), mostly

I’m not sure when I got the urge to make a CNC… or maybe it was always there. I did a summer job in a machine shop when I was 19, where I was given a minimum of training by the 88 year old machinist: “keep the pink things [wiggling his fingers] away from the sharp things [pointing to the milling machine’s cutting edge], boy.”

(I’m not joking. He did show me more things later, but only because I asked. He would mostly just motion for me to come over so he could tell me filthy jokes from the silent film era and cackle to himself.) read more

Training a real robot to play Puckworld with reinforcement learning

After I trained an agent to play “puckworld” using Q-learning, I thought “hey, maybe I should make a real robot that learns this. It can’t be that hard, right?”

Hooooooooo boy. I did not appreciate how much harder problems in the physical world can be. Examples of amateurs doing Reinforcement Learning (RL) projects are all over the place on the internet, and robotics are certainly touted as one of the main applications for RL, but in my experience, I’ve only found a few examples of someone actually using RL to train a robot. Here’s a (very abridged!) overview of my adventure getting a robot to learn to play a game called puckworld. read more

The Red Lama (Red Llama clone)

After making the worst fuzz pedal ever (that’s for another post) and Orange Ya Glad (which was fine, but didn’t add quite as much fuzz as I wanted and adds a weird buzz even when you’re not playing on some speakers), I just wanted a normal fuzz pedal. After doing a bit of reading, I found that the Red Llama overdrive pedal (by Way Huge) is a classic, and after watching a few YouTube demos, it seemed good (to be honest, people are crazy about the “different” sounds of various fuzz/distortion/overdrive that various antique/obscure transistors or configurations will give you, but they all sound pretty similar to me, and I suspect people think they’re hearing differences more often than there actually are).

Anyway, I wanted to tribute the original Red Llama circuit I was cloning, so I went for… read more

Some mathy tesselating stamp art!

I was recently at the art store for some reason, just browsing. I found the linoleum stamp section at the back and immediately wanted to make some! We had made them in 5th grade art class or something, and I remember liking it a lot, but had never since then. They’re kind of the perfect type of art for me, since I seem to like 3D things with more of a “crafts” element. I like carving/whittling anyway, so this was perfect.

I grabbed a few (pretty cheap), and on the way home thought of what I’d do: make a square stamp with weaving paths, asymmetric, such that it could be stamped out in a grid to either create cool repeating patterns, or random ones. read more

Neato sequence art

A while ago I watched this video by Numberphile (a very cool channel!). In terms of the actual math, it’s pretty light, but what I love about it is the idea of creating very cool images via very simple rules.

The idea of it is this. In the video, the guy shows a way of drawing a sequence of numbers (more on that in a minute). You draw a number line, and then you draw a semicircle above the line from the first number of the sequence to the second, with a diameter equal to the distance between the points. Then, you draw a semicircle from the second to third numbers of the sequence in the same way, except this time, the semicircle goes under the number line. You continue this way as long as you want, alternating above/below for the semicircles. read more

RPi camera, part 3: a few incremental fixes

Round 3! Okay, this is where I try and polish it up in a couple ways.

Here are the things I said last time I needed to make better: read more

Motion detection with the Raspberry Pi, part 2

Hi hi!

In this post, I’m really just going to concentrate on building the whole pipeline. It’s going to be rife with inefficiencies, inaccuracies, and stuff I 100% plan on fixing, but I think it’s good to get a working product, even if it’s very flawed. Someone I once worked for told me that projects in the US gov’t kind of work that way: there was high emphasis on getting a product out the door, even if it was hacky and awful (though hopefully not). I think that makes sense a lot of the time. It’s probably more motivating to see a project that does something to completion, even if it’s crappy, than a project that is partly carefully done, but still very incomplete. A crappy car is cooler than a really nice wheel. Also, it seems like iterative, smaller fixes are relatively easy. read more

Motion detection with the Raspberry Pi, part 1

Okay Declan, let’s try making this post a short and sweet update, not a rambling Homerian epic about simple stuff.

I got a Raspberry Pi (RPi) and an RPi camera because I wanted to learn about them and mess around with them. If I could do image recognition with them, that’d be a good platform to do ML, NN, and if I got enough data, maybe even DS type stuff. Luckily, there’s a ton of resources and code out there already. I drew upon heavily from www.pyimagesearch.com, which is a REALLY useful site, explained very great for beginners. Two articles that I basically copied code from and then butchered were this and this. read more

Kaggle Housing challenge, my take

In this article, I’m doing the Kaggle Housing challenge, which is probably the second most popular after Titanic. This was very much a “keeping track of what I’m doing for learning/my own sake” thing, but by the end I’ve gotten a ranking of 178/5419 on the public leaderboard (LB). That said, this is super long because I tried a million things and it’s kind of a full log of my workflow on this problem.

I’ve really learned a bunch from going through this very carefully. What I did here was to try the few techniques I knew when I started, and then I looked at notebooks/kernels for this challenge on Kaggle. A word on these kernels: even the very most top rated ones vary in quality immensely. Some are excellently explained and you can tell they tried different things to try and get an optimal result. Others are clearly people just trying random stuff they’ve heard of, misapplying relatively basic techniques, and even copying code from other kernels. So I viewed these as loose suggestions and guideposts for techniques. read more