A robot can have excellent software and still fail at a simple task. The program may issue exactly the right instruction, yet the wheels slip, a camera loses the object it was tracking, or a sensor reports a value that does not match the physical world. Software describes what the robot is meant to do. Whether that intention becomes a reliable result depends on sensors, actuators, mechanical design, the surroundings, safety controls, how the machine is integrated into a larger process, and how people work with it. Good code is necessary, but it cannot guarantee the outcome on its own.
The gap between a command and a result
Most software bugs are visible in the logic: a condition is wrong, a value is off by one, a function returns the wrong type. Robot failures often come from a different place. The code can be correct and the physical action can still fail, because the instruction “move forward 30 centimetres” assumes the floor grips, the sensors read accurately and nothing blocks the path. When one of those assumptions breaks, the program has no direct way to know unless it was designed to check.
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The clearest public statement of this point is a DEV Community article by Dominik Voger, which describes the problem with the phrase “A robot can have excellent software and still fail at a simple task.” The article is useful for its framing rather than for measured data: it treats robot failure as a matter of the physical world, not only of program quality.
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A robot builds its picture of the world from measurements that are never exact. Three common examples:
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- Slipping wheels. Odometry, which estimates position from wheel rotation, drifts when a wheel spins on a wet or loose surface. The software receives a clean encoder count, but the robot has moved a different distance than the count implies.
- Lost visual tracking. A camera-based system can lose an object because of glare, motion blur, a partial occlusion or a change in lighting. The detection step may simply return nothing, and the next action is then chosen on incomplete information.
- Imperfect sensor readings. Distance sensors, force sensors and cameras all carry noise and calibration error. A reading that looks plausible can still be wrong enough to make a grasp fail or a stop occur late.
Knowing whether an action worked
Issuing a command is not evidence that the action happened. A robot has to determine whether the result matches the intent and react when it does not. The DEV article points to stopping, obstacle avoidance and retrying as examples of this harder part. Each one requires the robot to compare what it expected with what it senses, then decide what to do with a mismatch. Retrying is especially delicate: a second attempt can succeed where the first failed, but it can also repeat a mistake, push an object further, or move while a person has entered the path.
Robotics is a stack, not a program
Software is one layer in a system whose behaviour depends on every layer working together. The table below lists the main layers, what each contributes and the typical way each one lets a task down.
| Layer | What it contributes | Typical way it fails a task |
|---|---|---|
| Software and control logic | Plans, decisions, and the commands sent to hardware | Assumes sensor data and motion are accurate; does not detect a mismatch |
| Sensors | Measure position, distance, force, images and other state | Noise, calibration drift, occlusion, glare or missing returns |
| Actuators and mechanisms | Turn commands into motion, grip, or force | Wear, backlash, slip, insufficient torque, or delayed response |
| Surroundings | The floor, objects, lighting, and other moving things | Conditions differ from the environment the robot was tested in |
| Safety controls | Limit speed and force, stop motion, and signal hazards | Set up for the wrong task, bypassed, or not tested under real conditions |
| System integration | Connects the robot to tooling, fixtures, other machines and processes | Interfaces and timing assumptions that do not hold on site |
| Human interaction | Operators set up, supervise, intervene and maintain the robot | Unclear interfaces, poor awareness of what the robot is doing, or misplaced trust |
The practical consequence is that a fix in one layer rarely settles the question. A better path planner does not help if the wheel encoders are unreliable, and a sturdier gripper does not help if the software never checks whether the part is still in the hand.
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Designing for failures you cannot predict
Engineers cannot list every way a physical task may go wrong, so the aim is to make failures detectable and their consequences bounded. The sequence below is an editorial pattern that many robot designs share; it is not a procedure drawn from any single standard.
- Define the expected result. Specify what success looks like in measurable terms, such as a gripper force range or a position tolerance, rather than only “the command was sent.”
- Verify with an independent signal. Confirm the outcome with a sensor or a second measurement where possible, and do not rely on the commanded value alone.
- Set a safe state. Decide in advance what the robot does on uncertainty: stop, hold position, reduce speed, or release a load.
- Limit retries. Allow a bounded number of attempts, and re-check the environment before each one.
- Escalate to a person. Log the failure and alert an operator with enough context to act, rather than letting the robot guess repeatedly.
Safety is assigned to scopes, not to code
Industrial robot safety is organised around standards that split responsibility by scope. Knowing which scope applies is the first step in judging any claim that a robot is “safe.” None of the standards below makes every kind of robot safe, and each has explicit exclusions.
ISO 10218-1:2025: the robot as a machine
ISO 10218-1:2025, published February 2025, addresses safety requirements for industrial robots as machines. It is robot-level: it concerns the machine itself, not the particular cell or application where it is used.
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ISO 10218-2:2025: the application and the cell
ISO 10218-2:2025, also published February 2025, addresses industrial robot applications and robot cells. Its scope includes integration, commissioning, operation, maintenance and decommissioning. This is where the gap described earlier becomes an engineering responsibility: a robot that is safe as a machine can still create hazards once it is installed with a particular tool, fixture and workflow.
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ISO/TS 15066:2016 describes safety requirements for collaborative industrial robot systems and supplements the guidance in ISO 10218-1 and ISO 10218-2. The ISO page states that it does not apply to non-industrial robots, and it displays a proposed withdrawal stage. Readers should check its current status with ISO before treating it as the standard for collaborative robots.
| Document | Scope | Status and publication | Stated exclusions or limits |
|---|---|---|---|
| ISO 10218-1:2025 | Safety requirements for industrial robots as machines | Published February 2025 | Excludes areas including consumer products, public-access service robots, and medical and healthcare robots; check the individual scope |
| ISO 10218-2:2025 | Industrial robot applications and robot cells, including integration and lifecycle activities | Published February 2025 | Same exclusion categories as Part 1; check the individual scope |
| ISO/TS 15066:2016 | Collaborative industrial robot systems; supplements ISO 10218 guidance | Published 2016; ISO page displays a proposed withdrawal stage | Does not apply to non-industrial robots |
Both 2025 standards also exclude lifting or transporting people. A service robot in a public space, a consumer device in a home, and a medical robot are outside the industrial scope, so the existence of an ISO number does not transfer to those settings.
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Testing has to cover the whole machine
Code review and unit tests check logic. They say little about whether a robot can climb a slope, grasp a damaged box or keep its position when a sensor drops out. The U.S. Department of Homeland Security and NIST response robot performance standards project takes a broader view. Its test methods cover mobility, manipulation, sensors, energy, communications, human–robot interfaces, logistics and safety.
The project states that these methods can support comparisons between robot models and training for operator proficiency. The public project page does not report numerical results that would rank any particular machine, and it should be read as a description of what is measured rather than as a verdict on any product. Its value for a deployment team is the list of capabilities it expects to be tested, which is wider than the software alone.
People are part of the system
Human interaction is often treated as an afterthought, yet it determines whether a robot’s failures are noticed in time. NIST’s Performance of Human-Robot Interaction project treats trust and safety, interface methods, and system and situation awareness as central concerns. The question for a team is whether an operator can tell what the robot is doing, why it stopped, and what it will do next.
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The project’s public materials do not establish a single universal measure of trust, and they do not guarantee that a given interface produces a safe outcome. Design decisions therefore have to be checked in the actual setting with the actual people who will use the robot.
A practical check before relying on a robot
- Ask which ISO scope applies to the robot, to its application cell, and to the setting where it operates; a document that excludes your setting does not cover it.
- Confirm the status of any cited standard on the issuing body’s page, since withdrawal stages and revisions change what applies.
- Identify the independent signals the robot uses to confirm each critical action, and what it does when they disagree.
- Test the full task under realistic floor conditions, lighting, object variation and sensor faults, not only in a clean laboratory run.
- Define the stop, hold and escalation behaviour in writing, and rehearse it with the operators who will supervise the robot.
- Repeat the checks after any change to the tooling, workspace, software or staffing.
Further reading
For the ethical and social side of autonomous machines, the edited volume Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence (2017) includes chapters on physical safety, responsibility and human–robot interaction. It is a useful complement to the engineering and standards material above, though its discussion predates the 2025 ISO revisions.
Robots are built from many parts that must agree with one another in a physical environment that never matches the model exactly. Good code is the place where intent is written down. Safety, reliable behaviour and trust come from whether the whole system can sense its own failures and respond to them.
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