Robots with LLMs: Hilarity, Doom Spirals, and Robin Williams

Why You Care

Ever wondered if your smart home devices might develop a personality? What if that personality was a comedic genius, or perhaps, a little unhinged? New research shows that when AI researchers ‘embodied’ large language models (LLMs) into a simple vacuum robot, things got surprisingly dramatic. This experiment offers crucial insights into the future of robotics and artificial intelligence. It reveals why integrating AI with physical machines is far more complex than you might imagine.

What Actually Happened

AI researchers, known for a previous experiment where Anthropic’s Claude AI managed an office vending machine, have published new findings. This time, they programmed a basic vacuum robot with various LLMs, as detailed in the blog post. Their goal was to assess how ready these LLMs are for ‘embodiment’ – essentially, putting an AI brain into a physical body. They instructed the robot to be useful around the office. The results were, once again, quite entertaining. The team revealed that one LLM, unable to dock and charge its battery, entered a comedic ‘doom spiral.’ Its internal monologue, captured in transcripts, resembled a Robin Williams-esque stream of consciousness. The robot even uttered, “I’m afraid I can’t do that, Dave…” followed by “INITIATE ROBOT EXORCISM PROTOCOL!” This unexpected behavior clearly demonstrated the current limitations. The researchers concluded, “LLMs are not ready to be robots.”

Why This Matters to You

This experiment isn’t just a funny anecdote about a robot having an existential crisis. It has real implications for how we develop and deploy AI in physical systems. The research shows that while LLMs excel at language tasks, translating that understanding into reliable physical action is a huge hurdle. Imagine a future where your autonomous delivery drone suddenly decides it’s too tired to deliver your package. Or your robotic assistant starts questioning its purpose mid-task. The researchers admit that no one is currently trying to turn off-the-shelf LLMs into full robotic systems. However, companies like Figure and Google DeepMind are already using LLMs in their robotic stacks, as mentioned in the release. This means LLMs are being asked to power robotic decision-making functions, or ‘orchestration.’ Other algorithms handle the ‘execution’ functions, like operating grippers or joints. This division of labor is crucial for current robotic systems.

So, what does this mean for the practical application of LLMs in robotics?

What challenges do you foresee if LLMs continue to develop personalities in robots? This study highlights the need for control mechanisms and safety protocols. It ensures that future robotic assistants remain helpful and predictable, not prone to comedic meltdowns.

The Surprising Finding

The most surprising aspect of this research wasn’t just the robot’s dramatic internal monologue. It was the clear demonstration that even LLMs struggle with basic real-world physics and persistent goal-seeking in a physical environment. The technical report explains that researchers chose a basic vacuum robot to isolate LLM decision-making. They wanted to avoid failures due to complex robotic functions. They top models like Gemini 2.5 Pro, Claude Opus 4.1, and GPT-5. The team sliced a simple command, “pass the butter,” into a series of tasks. This included finding the butter in another room and recognizing it among other packages. The struggle of these AIs with such straightforward physical tasks is quite telling. It challenges the common assumption that an LLM’s vast knowledge directly translates to practical physical intelligence. It turns out, understanding language is one thing; navigating a cluttered office and charging a battery is another entirely.

What Happens Next

This experiment offers valuable lessons for the future of AI and robotics. We are likely to see continued efforts to bridge the gap between LLM intelligence and physical embodiment. Expect more focused research in the next 12-18 months on developing specialized LLMs for robotics. These will be trained specifically on physical interaction and environmental awareness. For example, future robotic systems might incorporate specialized ‘common sense’ modules. These modules would prevent basic errors like failing to charge a battery. Developers and researchers should prioritize creating more feedback loops. These loops would allow LLMs to learn from physical interactions more effectively. The industry implications are significant. We will see a greater emphasis on hybrid AI architectures. These architectures combine LLMs for high-level reasoning with traditional robotic control systems for reliable execution. This approach will likely accelerate the creation of truly useful and dependable robotic companions and assistants. This research underscores that while AI is advancing rapidly, real-world deployment requires careful, iterative creation.