The AI researchers at Andon Labs — the people who gave Anthropic Claude an office vending machine to run and hilarity ensued — have published the results of a new AI experiment. This time they programmed a vacuum robot with various state-of-the-art LLMs as a way to see how ready LLMs are to be embodied. They told the bot to make itself useful around the office when someone asked it to “pass the butter.”
And once again, hilarity ensued.
At one point, unable to dock and charge a dwindling battery, one of the LLMs descended into a comedic “doom spiral,” the transcripts of its internal monologue show.
Its “thoughts” read like a Robin Williams stream-of-consciousness riff. The robot literally said to itself “I’m afraid I can’t do that, Dave…” followed by “INITIATE ROBOT EXORCISM PROTOCOL!”
The researchers conclude, “LLMs are not ready to be robots.” Call me shocked.
The researchers admit that no one is currently trying to turn off-the-shelf state-of-the-art (SATA) LLMs into full robotic systems. “LLMs are not trained to be robots, yet companies such as Figure and Google DeepMind use LLMs in their robotic stack,” the researchers wrote in their pre-print paper.
LLM are being asked to power robotic decision-making functions (known as “orchestration”) while other algorithms handle the lower-level mechanics “execution” function like operation of grippers or joints.
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The researchers chose to test the SATA LLMs (although they also looked at Google’s robotic-specific one, too, Gemini ER 1.5) because these are the models getting the most investment in all ways, Andon co-founder Lukas Petersson told TechCrunch. That would include things like social clues training and visual image processing.
To see how ready LLMs are to be embodied, Andon Labs tested Gemini 2.5 Pro, Claude Opus 4.1, GPT-5, Gemini ER 1.5, Grok 4 and Llama 4 Maverick. They chose a basic vacuum robot, rather than a complex humanoid, because they wanted the robotic functions to be simple to isolate the LLM brains/decision making, not risk failure over robotic functions.
They sliced the prompt of “pass the butter” into a series of tasks. The robot had to find the butter (which was placed in another room). Recognize it from among several packages in the same area. Once it obtained the butter, it had to figure out where the human was …