A person continuously controls some or all of the robot's actions from a distance.
Explained / Operating modes
A robot can move by itself.
That does not mean nobody helped.
Learn how teleoperation, supervision, intervention and autonomous execution differ—and which counts and time measures reveal the human support behind robot output.
01
Name the operating mode before judging autonomy
Autonomy is not a universal badge attached to a robot. It describes who performs which decisions and actions for a defined task, under stated conditions. The same robot may be autonomous for route navigation, teleoperated for manipulation and manually reset after an exception.
A source that only says autonomous does not tell us the task boundary, supervision model or amount of human support. Those details must remain separate instead of being compressed into one label.
The robot executes a defined task while a person monitors progress and can approve, redirect or stop it.
The robot executes the stated task without continuous human control or monitoring during the defined window.
02
Human present is not the same as human intervention
A nearby worker does not automatically make every cycle assisted. Conversely, a remote operator can materially control the work without standing beside the robot. Good reporting defines which human actions count as normal workflow, supervision, assistance, intervention or recovery.
Training must also be separated from evaluated operation. Teleoperating a robot to demonstrate a task may be part of data collection; it is not automatically an intervention during a later autonomous run.
A person watches the work and remains available, but may never alter a task cycle.
Expected human input is built into the normal workflow, such as starting a batch or approving a planned step.
A person acts to keep, restore or redirect work after the robot cannot continue as intended.
The robot or a person returns the task to normal operation after an exception or failure.
03
Measure the human work, not just the robot's output
A useful autonomy disclosure needs counts and time. State the operating mode for each task phase, the number and duration of interventions, who supervised how many robots, and whether assisted output is included in the headline result.
The denominator must travel with the rate. Ten interventions across ten attempts describe a very different system from ten interventions across ten thousand attempts, even though the intervention count is identical.
Unassisted task completions divided by all defined attempts.
Qualifying interventions per task attempt, operating hour or another stated denominator.
Human interaction time divided by the same bounded task or operating period.
04
A worked example
Imagine 100 defined task attempts: 80 finish without help, 12 finish after a remote correction, 5 finish under direct teleoperation and 3 do not finish. This is a hypothetical example, not a result for a robot in the database.
The workflow produced 97 completed tasks, but it did not achieve 97% autonomous completion. A transparent report preserves all four outcomes and states whether the 12 corrections count as interventions, how long they took and what triggered them.
Finished without help.
Finished after a remote correction.
Finished under direct human control.
Did not meet the completion rule.
05
How HumanoidUptime reports autonomy
HumanoidUptime records the operating mode a source actually supports. A product claim, a teleoperated demonstration, supervised task execution and measured autonomous-time share remain different evidence. Words such as deployed, working or autonomous do not fill in a missing mode breakdown.
When a source discloses mixed autonomous and teleoperated execution but gives no shares, the record says exactly that. When intervention logs are not public, the intervention rate stays undisclosed rather than being treated as zero.
For Apollo at Mercedes-Benz, operator-confirmed teleoperation describes how employees transferred task knowledge during training. It does not establish how often people intervened during autonomous execution.
References
Technical grounding
- NISTReference
Terminology for Specifying the Autonomy Levels for Unmanned Systems: Version 1.0 ↗
NIST terminology for human-robot interaction, operator roles and autonomy-level measurement.
- NISTReference
Autonomy Levels for Unmanned Systems (ALFUS) Framework ↗
NIST framework covering operating modes, human roles and measurable human-independence factors such as intervention time relative to mission time.
- NISTReference
Measurement Science for Robotics and Autonomous Systems Program ↗
Current NIST measurement program emphasizing application-specific metrics, human-robot interaction and teleoperation across different autonomy levels.
Next step
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on a real record.
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