Self plug: I'm the founder of Clipper, a container registry that has 10x faster pulls and 7x faster builds over DockerHub, targeted at robotics. I got tired of robotics deploys taking all day and fixed it myself. If you're interested in that or just want to talk shop, let's chat.
- I've had someone in the Docker discord tell me at length that my container shouldn't be bigger than 1GB, which is impossible the moment you pull in CUDA or any ML libraries.
- Robots pull over 3G/Starlink/slow customer WiFi
- Robots are edge devices! They have previous related images, they aren't pulling onto a fresh disk like on web.
Clipper solves this by breaking apart Docker layers and indexing the files within. When you pull an image it will reuse related files in images you already have on your device. This has other side benefits as well, like being able to mount layers as networked filesystems and much more easily being able to run P2P updates.
It's less that robotics needs a different container registry and more that robotics needs a better container transport, and I've built the infrastructure around it for my registry.
> A few centimeters of error may not matter much when navigating down a hallway, but it matters if a sensor is supposed to remain normal to a curved surface.
This is a great specific example of where the creativity (read: randomness) of AI runs into a wall. Do you see this changing over time as models improve, or do you expect that safety-critical / highly specific tasks will always require a more explicit set of instructions?
If so, as someone who lives close to Salem, I like it :)
Good luck to you guys.
> We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible. We're interested in the marriage between the two rather than trying to make every part of the robotics stack learned.
Seems to me is that if you restrict the problem space, have well defined tasks, then marry the semantic understanding and reasoning of VLMs with the geometric tools of classical approaches, then you could have something practical and economical.
A shift from a single company handling all software and hardware inhouse, to a sort of divergence of the two. Kind of cool to think about, given how that sort of divergence seems to have happened in a few other areas (a dell computer runs windows, an LG TV runs apple TV, your iphone runs airbnb, spotify, instagram)