Your Robot’s Safety Functions Already Work. What If the Input Lies?

This article is brought to you by VicOne . A mobile robot slows for someone in a hallway. A collaborative arm eases its movement as a worker approaches. A humanoid pauses to let a person pass. Each response depends on information about the robot or its surroundings: a distance reading, a position estimate, or a stop signal. But what happens when one of those inputs is wrong, and the robot still responds exactly as designed? The mobile robot reads the hallway as clear while someone is still there. The arm estimates that the worker is farther away. The humanoid does not receive a stop signal as a person crosses its path. In each case, a fault could cause that mismatch. So could deliberate manipulation . At VicOne LAB R7 , we examine how manipulated inputs can change robot behavior — and what that means for the systems meant to keep people safe. How an untrusted input can change a robot’s behavior A robot does more than measure its surroundings. It may interpret what it sees and hears to decide what to do next. Information placed in its environment can therefore influence its actions. Published research shows how this can happen. In a study of vision-language-action (VLA) models , a patch in a camera’s view reduced task success in simulated robot tests. FreezeVLA found that an adversarial image could cause tested models to ignore later instructions. Although the studies used different methods, both demonstrate how visual input can interfere with a robot’s intended task. In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’

Manipulated inputs can cause robots to behave unsafely.

SOURCE     IEEE Spectrum: AI (tier 1)
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DOCUMENT   39726407-27c2-4b58-853d-9944387fc522
           https://spectrum.ieee.org/ai-robot-safety-cybersecurity-vicone
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EVIDENCE   2 extracted excerpts
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MODEL RUN  1 run, methodology 1.0.1
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SCORE      -50  (Adverse)
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CONFIDENCE 80%