Comment by sho_hn | Hacker News Reader

Comment by sho_hn | Hacker News Reader

Anyone got any fun stories to share of trying to use LLaVA e.g. to make toy robots navigate? How good is it at outputting directions in structured data, guess distances, angles, etc.?

My weekend hacking goal would be something like an RC car that can "drive to the largest plant in the room" or "go hide under the dining table" when prompted by voice. Slowly, by combining some sort of basic SLAM with still image promoting.

This looks quite promising: https://i.imgur.com/DnYWYPl.jpeg

Of course an alternative to doing it one-shot would be to collect lots of pictures + orientation for each, have LLaVA only caption them, then prompt a more generic LLM with that collected world info to pick where to go, etc.

What I like most about this AI stuff is how many neat things it makes achievable in a weekend by a motivated hobbyist that in the past required entire companies to tackle :). DIY/maker life in the AI age has been amazing fun do far.

Apologies, this was the wrong image upload, and it's now too late to edit the post. The intended one was a screenshot of a LLaVA 1.6 demo conversation about it:

https://i.imgur.com/J4yZ8xH.png

My best guess is that you want a supervisor GPT-4 like LLM planning the task and a lower level on-prem LLM doing the tasks like driving from one location to another or grasping an item.

Sending every frame to GTP-4 right now is way too slow. But at Tesla FSD like model can drive from one location to another in a closed environment with perfection. All that is missing is training that style in a roomba/robot form and then having GTP-4 monitor and manage the tasks at a 10 or 20 second interval

Oh, I'm OK with going slow! It doesn't have to be all that practical, I'm more curious about playing with toy approaches. Trying to populate a world model with few captioned still frames plus basic IMU/dead reckoning seems like a fun challenge ...

Reminds me of the recent HN post on jumping spider intelligence: They can do complex route planning, but need to stare for hours before they get going. This is probably more down to their tiny field of view on the front-facing good eyes, but still :-)

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