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Comment by porphyra | Hacker News Reader

That's super cool and I hope you don't mind a little bit of unsolicited feedback but the first question everyone's asking is "what does it do?" At present the blog post starts out with two paragraphs talking about the format of the blog post and the applications but not what the sensor actually measures.

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That's basically every other HN article for me. Zero context. "Blorglorp 2024.4.99 released" "With the new version, Blorglorp finally sheds its libgnipgnop dependency and increases efficiency by 1.25%". Bam, top post for the day, lots of multi-paragraph comments, and I'll still never know what it's even for.

The context tends to come some time later, when they close store. "We at Derplabs are proud that we dared to make an opinionated jpeg viewer, Blorglorp, that only interpreted the four first bits of every byte and ignored the rest. Commonly referred to as 'the naughty bits' by image-viewing connoisseurs. Unfortunately the market was not ready, but we are sure our ideas will gain traction in the future. Our deepest gratitude to our customers and investors that were excited to join our journey."

You think? All the closing announcements I've seen were "we've reached the end of our incredible journey, we're proud to have served our users but your data is gone tomorrow. Good luck!".

Meanwhile, I never find out what the thing even does.

I can die happy if I never have to work with libgnipgnop again

But have you used the new 3.6 release which uses pfnaphell integration to stochastically pre-convolute the tertiary nodes? It's a game-changer!

lol v3.6.7845 doesn’t even self calibrate. Most people are on v3.6.8002a except Mac users and people using a venv. Works perfectly under newer plan9 emulators. I just use Albus mode in Emacs (trunk) and avoid a lot of those problems. If you forget to use trunk the mouse is disabled for some reason…

Good point -- I need to better explain what "bend localization" means on a more practical level pretty early on.

Yeah. I guess in layman's terms it's a long squishy cable that knows where it's being bent.

> This means the sensor can tell you where you bent it, with a predefined (and coarse) resolution.

> OptiGap’s application is mainly within the realm of soft robotics, which typically involves compliant (or ‘squishy’) systems, where the use of traditional sensors is often not practical.

This explanation is already quite clear. If I understood correctly, by "predefined resolution" you mean that it detects which silicone sleeve was bent on a tube with a series of them, correct?

Can you provide more concrete examples for how you envision it being used? The first application that comes to mind is sensing how fingers bend in a glove controller.

The finger bending example is certainly a classic for something like this but I think it truly shines in soft robot examples like flapping wing robots or swimming finned robot, where it's critical for sensors to be mechanically transparent so as to not impact the usually delicate dynamics. The "soft" robotic arm in my earlier paper is another good example https://ieeexplore.ieee.org/document/9763962

Also; if you can please include examples of practical use cases. I am sure there are tons, but I always love discovering complexity in hidden places

Have a quick recording that shows an outcome or effect that is a core part of the research.

Having just read the piece for the first time after you added the "bent rope" explanation: YOU NAILED IT. I literally had the reaction of thinking, "Wow, there's a super simple explanation early on! I trust this writer much more now."

One application for shape sensing technology is for surgical robotics. An example: https://www.intuitive.com/en-us/products-and-services/ion/sh...

Relevant patent: https://patents.google.com/patent/US20240044638A1/

The first time I saw one of these in person I was in awe. You could take a normal looking cable (think bicycle cable sleeve) and bend it and see in real time the same shape on the display.

This question surprises me. Bend location == locate where something is bent. Then some video's of the researcher bending a tube. Is there any confusion possible?

OP updated his blog post after I posted my comment. The opening paragraphs are now very clear and awesome.

Oh - thanks for pointing this out!

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