Our LLM-controlled office robot can't pass butter (andonlabs.com)
Hi HN! Our startup, Andon Labs, evaluates AI in the real world to measure capabilities and to see what can go wrong. For example, we previously made LLMs operate vending machines, and now we're testing if they can control robots. There are two parts to this test:
1. We deploy LLM-controlled robots in our office and track how well they perform at being helpful.
2. We systematically test the robots on tasks in our office. We benchmark different LLMs against each other. You can read our paper "Butter-Bench" on arXiv: https://arxiv.org/pdf/2510.21860
The link in the title above (https://andonlabs.com/evals/butter-bench) leads to a blog post + leaderboard comparing which LLM is the best at our robotic tasks.
30 comments
[ 4.3 ms ] story [ 58.5 ms ] threadNearly as good as my resource booking API integration that claimed that Harry Potter, Gordon the Gecko and Hermione Granger were on site and using our meeting rooms.
ERROR: Success failed errorfully
ERROR: Failure succeeded erroneously
ERROR: Error failed successfully
But I suppose that if you can train an llm to play chess, you can also train it to have spatial awareness.
was the script of Last Tango in Paris part of the training data? maybe it's just scared...
But it seems pretty obvious to me that after decomposition and parameterization, coordination of a complex task would much better be handled by a classical AI algorithm like a planner. After all, even humans don't put into words every individual action which makes up a complex task. We do this more while first learning a task but if we had to do it for everything, we'd go insane.
Or to put it another way, if the writings of humans who have lost their minds (and dialogue of characters who have lost their minds) were entirely missing from the LLM’s training set, would the LLM still output text like this?
> if the writings of humans who have lost their minds (and dialogue of characters who have lost their minds) were entirely missing from the LLM’s training set, would the LLM still output text like this?
I think should distinguish between concepts like "repetitive outputs" or "lots of low-confidence predictions the lead to more low-confidence predictions" versus "text similar to what humans have written that correlates to those situations."
To answer the question: No. If an LLM was trained on only weather-forecasts or stock-market numbers, it obviously wouldn't contain text of despair.
However, it might still generate "crazed" numeric outputs. Not because a hidden mind is suffering from Kierkegaardian existential anguish, but because the predictive model is cycling through some kind of strange attactor [0] which is neither the intended behavior nor totally random.
So the text we see probably represents the kind of things humans write which fall into a similar band, relative to other human writings.
[0] https://en.wikipedia.org/wiki/Attractor
But boy am I glad that this is just in the play stage.
If someone was in a self driving car that had 19% battery left and it started making comments like those, they would definitely not be amused.
waiting for the huggingface Lora
Someday, and given the billions being thrown at the problem, not too far out, someone will figure out what the right tool is.
I think the real value of llms for robotics is in human language parsing.
Turning "pass the butter" to a list of tasks the rest of the system is trained to perform, locate an object, pick up an object, locate a target area, drop off the object.