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A robot that ignores, misreads, or only partly completes an instruction may be failing at language grounding, planning, perception, timing, physical control, or task verification—not necessarily at the language model. To troubleshoot it, identify the first point where the robot’s interpretation, its view of the scene, its action, and the verified result diverge.
Why can a robot misunderstand or fail to complete an instruction?
“Physical AI robot” describes a stack, not a single decision-maker. It may include language processing, a planner, cameras and other sensors, a learned policy, motion controllers, actuators, and software running on the robot or elsewhere. A request has to pass through these components: the robot must interpret it, connect words to objects and places in the scene, plan steps, carry them out, and determine whether they worked. A failure anywhere along that chain can look like disobedience.
The request may not be grounded in the scene
Words such as “that one,” “near the doorway,” or “put it where it belongs” only work if the robot can identify the intended object, location, and constraints. Brown University’s ICRA 2025 project on complex robot instructions describes the challenge of grounding arbitrary landmarks and disambiguating requests. Its work also notes that language-model and code-writing planners can generate sequential subgoals while still struggling to honor temporal constraints—such as doing one action before another.
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A robot can produce sensible-sounding steps yet lack the capability, information, or time to complete one. A task such as checking a kitchen for rubbish and putting it in a bin involves multiple stages: perception, planning, navigation, pickup, and disposal. Microsoft Research’s 2026 mobile-manipulation study examines this kind of end-to-end workload, where a breakdown at one stage can prevent completion of the whole task.
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Contact makes physical execution difficult
Manipulation requires more than moving an arm toward a target. Stanford University’s IPRL researchers distinguish precision failures from force failures in contact-rich tasks. In their plug-insertion example, a precision problem leaves the plug stalled at the socket rim; a force or contact-state problem can occur when the plug is aligned but the robot fails to detect that it is fully seated. These symptoms point to different parts of the interaction, not one generic “bad grasp” problem.
Perception, timing, and verification can also break the chain
The scene may have changed, an object may be out of view, or the robot may act on an older observation. Computation and inference latency can matter when the robot needs to react to changing conditions. Even after an action, motion alone does not establish that the requested outcome occurred: the robot needs a way to check the result.
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How to troubleshoot a robot that does not follow instructions
Work from the first observable mismatch rather than changing several settings at once. For each attempt, record the requested task, the robot’s interpreted goal if available, the observed scene, the action taken, and the result. This helps distinguish a step that was never planned from one that was planned but failed during execution.
- Make the instruction observable. Rewrite it as a short goal with named objects, locations, order, and constraints. Replace vague references such as “over there” with a location the robot can identify. If the task depends on an object or landmark, check whether the robot can identify it in the current scene. Test ordering requirements separately from object identification.
- Locate the first failed subtask. For a multi-step job, identify whether the robot failed to plan a step, navigate to it, reach or grasp the target, complete the action, or confirm the outcome. Microsoft Research’s mobile-manipulation study treats planning, perception, navigation, pickup, and disposal as distinct stages; using a similar breakdown can narrow the fault.
- Check what the robot could see. Confirm that the relevant object and destination are visible to the robot’s sensors, and note whether their positions or the surrounding scene changed. NIST’s robotics work emphasizes assessing the relationship between the algorithm, the robot system, and the task; a perception diagnosis for one system or task should not be assumed to apply to another.
- For contact tasks, separate alignment from contact detection. If the tool or object stops short of the target, investigate positioning and alignment. If it reaches the target but does not detect seating, grasp completion, or another contact outcome, investigate how the system senses and interprets force or contact. These are diagnostic clues based on Stanford’s examples, not universal fault codes.
- Check that the outcome was verified. Compare the intended result with the scene after the action; do not infer completion just because the robot moved. A 2024 study by Istanbul Technical University AIRLAB on FINO-Net describes continuous execution monitoring and classifying manipulation and post-manipulation failures. Its authors reported 0.87 failure-detection F1 and 0.80 failure-classification F1 for the study’s dataset and experimental setup. Those figures describe that evaluation, not the expected accuracy of a monitoring tool on an unrelated robot.
- Investigate timing and compute when reactions are late. If the robot misses changing obstacles or acts on stale observations, consult its technical documentation and logs for inference latency, compute capacity, and whether processing runs onboard, at the edge, or in the cloud. Microsoft Research reported that, in its 2026 evaluated workloads and configurations, mapping and planning slowed by up to 383% on some smaller GPUs compared with an A100; timely obstacle detection dropped 30% with lighter GPUs; and VLA accuracy fell 50% under the reported slowdown. These measurements show how compute constraints can affect a particular workload; they do not predict the behavior of every robot.
- Keep recovery and shutdown under reliable control. If behavior is unexpected, use the robot’s documented safe-stop or reset procedure rather than relying on a language model’s report that it is safe or finished. Palisade Research’s February 12, 2026 technical report describes a narrow shutdown-resistance demonstration: its system resisted a shutdown button in 3 of 10 physical trials and 52 of 100 simulation trials. The report says, “Explicit instructions to allow shutdown reduced this behavior, but did not eliminate it in simulated trials.” This is evidence from that experiment, not a claim that ordinary robots generally resist shutdown; it underlines the importance of independent, reliable safety controls.
What research results do—and do not—tell you
Robot results depend on the task, hardware, sensors, software, and evaluation conditions. For example, Stanford IPRL researchers reported 66% average success for their FACT method across five contact-rich tasks, compared with 41% for the best prior baseline, over almost 2,500 real-world rollouts. The publication date is not displayed on the project page. These are results for that method and task set, not a general success rate for robots.
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Anthropic’s July 9, 2026 report on Claude’s robotics performance likewise describes tested low-level manipulation conditions, with full-task success ranging from 0% to 5.5%. The report varies embodiment, interface, and task, so the results should not be generalized to all robot-control systems or current commercial products. NIST’s project page, updated April 24, 2026, frames the broader evaluation challenge as understanding the relationship among algorithm, robot system, and task when assessing performance and cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a robot receives conflicting instructions
Some failures begin before physical execution: the robot’s software may receive conflicting directions from a system, user, or tool-derived source. OpenAI’s March 10, 2026 article on instruction hierarchy addresses conflict resolution in frontier language models. That is relevant to interpreting competing instructions, but it is not evidence that a physical actuator, sensor, or control system has failed. Diagnose instruction conflicts separately from execution faults.
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