Shoutout to people who don't overthink blog setups. "thisismypersonalblog.com" AND GO. It'll do the job.
On a technical note, your setup is not far from where professional setups are headed [1]. Astra is really doing an end-run around (for now) research Robotics setups.
I also got a SO-101 and mucked about a bit. I stopped partly because I realised my idea of putting a laser on the end of it had potential risks but also when you’re just prompting an agent to do things it’s hard to feel that you’re really “learning robotics”.
I would rate that as an AI-assisted setup for a traditional research robotic workspace. No different than "Claude set up ci/cd for this project", just much more hands-on (pun intended). Software ate the world so anything that can generate software is going to make world-space interactions easier.
First law of calibration: Make sure you do an error analysis.
Finding the table surface is pretty useless using a top-down view, even with April tags, because the range error to an april tag is much more than the bearing (pixel) error to the april tag. You basically have trouble observing the thing you're trying to measure.
If you do this again, ask your agent to conduct this analysis and make sure your desired calibration variables are observable with small error. A second camera from a 45deg angle or even on the table would go a lot further, but then of course other things become unobservable.
Nice workaround using a proxy for force sensing to get touch info, however. And neat project overall!
This is very useful info! I've added the additional 45 degree camera and so far it's seems like there is a regression on skill but I think it's because it wasn't available in the earlier parts of the training.
Can you elaborate on the calibration variables comment? This sounds useful but how would I apply these observations?
Shoutout to people who don't overthink blog setups. "thisismypersonalblog.com" AND GO. It'll do the job.
On a technical note, your setup is not far from where professional setups are headed [1]. Astra is really doing an end-run around (for now) research Robotics setups.
[1]: https://x.com/ihorbeaver/status/2104646736652447854?s=20
I also got a SO-101 and mucked about a bit. I stopped partly because I realised my idea of putting a laser on the end of it had potential risks but also when you’re just prompting an agent to do things it’s hard to feel that you’re really “learning robotics”.
This post has given me some inspiration though!
I would rate that as an AI-assisted setup for a traditional research robotic workspace. No different than "Claude set up ci/cd for this project", just much more hands-on (pun intended). Software ate the world so anything that can generate software is going to make world-space interactions easier.
First law of calibration: Make sure you do an error analysis.
Finding the table surface is pretty useless using a top-down view, even with April tags, because the range error to an april tag is much more than the bearing (pixel) error to the april tag. You basically have trouble observing the thing you're trying to measure.
If you do this again, ask your agent to conduct this analysis and make sure your desired calibration variables are observable with small error. A second camera from a 45deg angle or even on the table would go a lot further, but then of course other things become unobservable.
Nice workaround using a proxy for force sensing to get touch info, however. And neat project overall!
This is very useful info! I've added the additional 45 degree camera and so far it's seems like there is a regression on skill but I think it's because it wasn't available in the earlier parts of the training.
Can you elaborate on the calibration variables comment? This sounds useful but how would I apply these observations?