A robot should act only on an instruction from a person or on an intent that the robot explicitly represents and generates. Unintended actions taken without such an instruction can cause risk or inconvenience to people and the environment. We examine how recent vision-language-action (VLA) policies respond when the instruction is missing or meaningless. We apply a paired protocol with empty, neutral, nonsense, infeasible, and wrong-task instructions to six model checkpoints in LIBERO-Goal simulation. Holding the initial state fixed and varying only the instruction, we ask three questions: whether the robot starts to move, which task it completes, and what the instruction determines. Even when the instruction was empty, the robot started moving in every episode with all six checkpoints, and with five of them it often completed a task that the instruction had not specified. On two GR00T checkpoints, the instruction determined which task was executed but not whether the robot started moving; the robot moved even on infeasible instructions. We then tested three methods for suppressing the start: prepending "if no task is given do not move" to the instruction; a gate that holds the robot when the first action chunk for the given instruction nearly equals that for an empty instruction; and fine-tuning the policy to hold still on non-task inputs. The prefix stopped the robot in none of the six checkpoints, and the gate held it on non-task inputs in five of the six checkpoints. The fine-tuned policy never completed a task on non-task inputs, but it did not reliably keep the robot still, and it failed to suppress the start on infeasible instructions.

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