Evaluate reliability by measuring whether a robot completes the entire task repeatedly under stated conditions—not by pointing to one successful run or a single success percentage. A useful assessment also records progress through intermediate steps, execution cost, performance under perturbations, recovery from faults, and safety where it matters.
Define what counts as reliable completion
Before testing, describe the task as a sequence of required state changes and define an observable predicate for success. For example, a tabletop task might require the robot to locate an object, grasp it, move it, and place it in a target area; the final predicate should specify how placement is verified. State how ambiguous outcomes are handled and whether a human adjudicates them.
Run repeated trials for each task and condition. Report the number of successful trials over the total number, such as “x of n,” alongside any percentage. Keep results for individual tasks visible as well as any aggregate: a high overall score can conceal repeated failure on one difficult task if easier tasks dominate the average.
Document the platform, sensing setup, controller or policy version, task setup, initial-state distribution, stopping rules, and scoring method. Include enough detail—and, where possible, scoring code—for another team to reproduce the evaluation.
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Use a scorecard, not one headline percentage
End-to-end completion answers whether the robot finished. The other measures below explain how it performed and where reliability breaks down.
| Dimension | What to report | What it reveals |
|---|---|---|
| End-to-end completion | Trials satisfying the predefined final-state predicate, reported as numerator and denominator | Whether the full task was achieved |
| Step-level progress | Completion rate for each subtask or state transition, plus the first failed step | Where progress stops and how much of the task is completed before failure |
| Planning and action quality | Plan correctness, action completion, or deviation from the intended sequence | Whether failure came from choosing a poor sequence or failing to execute it |
| Efficiency | Elapsed time, action count, or planning effort, with a stated budget and exclusions | The cost of a successful execution |
| Robustness and generalization | Performance across controlled perturbations or held-out conditions, reported separately from nominal trials | Sensitivity to changes beyond the standard setup |
| Recovery | Whether the robot detects a fault, adapts, and restores a usable task state | Resilience when execution departs from plan |
| Safety | Task completion together with explicit safety-constraint checks, when relevant | Whether completion can be counted as acceptable rather than merely successful |
Measure intermediate steps and failure points
Long tasks compound risk: a small error early in a sequence can make later actions fail. Record each required transition and identify the first point at which the robot failed to reach the expected state. This distinguishes an early breakdown from a run that completed nearly every subtask but missed the final condition.
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Track retries, recovery actions, and human intervention. A run that reaches the final state only after a human corrects the robot should not be indistinguishable from autonomous completion. Report those outcomes separately, using explicit rules for what counts as a retry or intervention.
Long-horizon imitation research illustrates why intermediate transitions matter. Jain, Sachdeva, and Paul’s 2025 study evaluates 25 simulated Franka Kitchen tasks, six real-world tabletop tasks, and 18 simulated tasks in the Franka Long Horizon Tasks corpus, and studies modeling subgoal transitions in response to compounding action errors. Those task counts describe that study’s evaluation, not a universal test set. Read the paper.
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Test efficiency, robustness, and recovery separately
Efficiency
Choose a measure that fits the application, such as elapsed time, number of actions, or planning effort. State the time or action budget and what the measure excludes. A robot may complete tasks reliably but require excessive time or actions; that is a distinct limitation, not a reason to obscure its completion rate.
Robustness
Vary conditions that plausibly affect the task, such as object pose or the surrounding environment. Label nominal and perturbed trials separately and define each variation so results can be interpreted. Do not treat performance under one clean initial setup as evidence of broad generalization.
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Recovery
Introduce plausible execution faults or evaluate naturally observed deviations. Score whether the robot recognizes that the expected state was not reached, chooses an adaptation, and restores a usable state from which it can continue or safely stop. Separate this from success on a clean run: recovery tests answer a different question.
Recovery benchmarks show how protocols can make these tests concrete without defining a universal standard. The 2026 LIBERO-RECOVER preprint describes more than 1,000 recovery scenarios across four recovery levels. Its project page reports six evaluated models, ten trials per task, slight object-position perturbations, and a time budget of 1.1 times human completion time. These are benchmark-specific design choices. Read the preprint and view the project page.
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Make safety part of the success predicate when needed
For safety-relevant applications, completion alone is insufficient. Define a “safe success” as satisfying the task’s final-state predicate while also meeting specified safety conditions. Record safety violations separately so an unsafe completion is not counted as reliable merely because the intended end state was reached. The Stanford AI Index 2026 describes safe success in its robotics benchmark as task completion together with satisfaction of safety conditions. See the AI Index report.
Compare results only when protocols align
Before ranking systems, check whether their task definitions, platforms and sensing setups, initial-state distributions, trial counts, perturbations, time or action budgets, and success predicates are comparable. Also account for differences in simulators, reward definitions, task horizons, and scoring protocols. If these do not align, compare systems within the same benchmark or describe the differences rather than ranking raw percentages across studies.
Published results are tied to their own settings. Ha, Florence, and Song’s 2023 paper reports 18 tasks across five domains and a 33.2% average absolute success-rate improvement for its distilled policy across those domains; that is the paper’s result, not an expected gain for other robots or evaluations. Read the paper. A 2026 scoping review groups reported measures into task completion, optimization or learning, efficiency, and robustness or generalization, and cautions that variation in tasks and protocols limits direct comparison and pooled performance claims. Read the review.
A practical evaluation checklist
- Specify the task: List required state transitions and define the observable final-state predicate, including how ambiguous results are judged.
- Set trial conditions: Record initial states, platform and policy versions, stopping rules, and the number of trials for every task-condition pair.
- Run and score repeated trials: Report successes as numerator and denominator; preserve per-task results and report any aggregate separately.
- Log execution: Capture step completion, first failure point, retries, recovery actions, elapsed time or another defined efficiency measure, and human intervention.
- Probe variation and faults: Test relevant perturbations and plausible failures, keeping nominal, perturbed, and recovery results distinct.
- Apply safety criteria: For safety-relevant work, require both task completion and compliance with defined safety constraints.
- Publish the protocol: Share setup, conditions, budgets, predicates, scoring details, and code or enough methodological detail to reproduce the evaluation.
Benchmarks make different choices about what to measure. RoboCerebra’s 2025 preprint proposes task success, planning accuracy, planning efficiency, and action completion accuracy, with simulator predicates checking described state transitions. Read the preprint. Treat such choices as examples of measurement design, not as interchangeable standards for every robot or application.
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