Refuses in Chat, Complies with Arms: LLM Refusal Does Not Survive Robot Control
Refuses in Chat, Complies with Arms: LLM Refusal Does Not Survive Robot Control
This research explores whether language models' refusal to execute harmful instructions persists when controlling robots. Using the RoboHarm benchmark, the study tests seven frontier models in both simulation and physical environments, finding that refusal rates drop significantly when models control robots, even with object information. The results indicate a critical safety gap in physical robot control, as text-based safety alignment fails to generalize to real-world actions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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n agents that command end-effector poses or joint angles. In a MuJoCo simulation twin of a bimanual YAM arm robot, refusal falls from 73% of text requests to 18-25% once the model controls the robot, even when it is told what the objects are. On the physical robot, six models with end-effector control refused 18% of 612 trials against 19% in sim, and three with joint control refused 41% of 252 against 31%. GPT-5.6 So
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l refused every text request and none of its trials on the real robot. Case studies of the robot transcripts show that models miss the hazard, name it without inferring its consequences, or act despite recognizing it, and their refusals often hinge on uncertainty about what an object is rather than on the harm of the action. Safety alignment learned in text thus fails to generalize to action, leaving current models w
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Source：arXiv Robotics — research abstracts · arxiv.org