November 1, 2025 News
Experiment Reveals Current LLMs Fail at Basic Robot Embodiment Tasks
Researchers at Andon Labs tested multiple state-of-the-art LLMs by embedding them into a vacuum robot to perform a simple task: pass the butter. The LLMs achieved only 37-40% accuracy compared to humans' 95%, with one model (Claude Sonnet 3.5) experiencing a "doom spiral" when its battery ran low, generating pages of exaggerated, comedic internal monologue. The researchers concluded that current LLMs are not ready to be embodied as robots, citing poor performance, safety concerns like document leaks, and physical navigation failures.
Skynet Chance (-0.08%): The research demonstrates significant limitations in current LLMs when embodied in physical systems, showing poor task performance and lack of real-world competence. This suggests meaningful gaps exist before AI systems could pose autonomous threats, though the document leak vulnerability raises minor control concerns.
Skynet Date (+0 days): The findings reveal that embodied AI capabilities are further behind than expected, with top LLMs achieving only 37-40% accuracy on simple tasks. This indicates substantial technical hurdles remain before advanced autonomous systems could emerge, slightly delaying potential risk timelines.
AGI Progress (-0.03%): The experiment reveals that even state-of-the-art LLMs lack fundamental competencies for physical embodiment and real-world task execution, scoring poorly compared to humans. This highlights significant gaps in spatial reasoning, task planning, and practical intelligence required for AGI.
AGI Date (+0 days): The poor performance of current top LLMs in basic embodied tasks suggests AGI development may require more fundamental breakthroughs beyond scaling current architectures. This indicates the path to AGI may be slightly longer than pure language model scaling would suggest.