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A robotics demo proves that a behavior happened under the conditions selected for the video. It does not prove that the robot can repeat the behavior, recover from errors, handle unfamiliar objects, operate fast enough to earn money, or fail safely. Evaluate the company by requiring repeatable evidence in the environment where it claims the system will work.
The most useful diligence covers six dimensions: reliability and failure modes, transfer to new situations, operational performance and cost, safety under uncertainty, separation of AI from human or engineered assistance, and deployment maturity. A credible company can state exactly what was tested, how success was defined, how many attempts were made, what interventions occurred, and what happened when the system failed.
Start with a testable claim, not a compelling clip
Ask the company to turn every capability statement into a claim that another party could reproduce. “Our robot understands warehouse work” is not testable. “Robot X, running software version Y, identifies and places 20 specified parcel types from a tote under these lighting and reach conditions, with no remote operator, at this cycle time and success rate” is testable.
For each claimed behavior, request a written test specification containing:
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- Task: the exact action and the acceptable end state.
- Robot and software: model, sensors, end effector, controller, AI model version and any tuned parameters.
- Operating conditions: lighting, floor and shelf geometry, object presentation, temperature, network connection, payload, battery state and permitted human proximity.
- Trial protocol: number of attempts, randomization, reset procedure, warm-up period and whether trials were selected or consecutive.
- Success definition: what counts as completion, partial completion, damage, timeout or unsafe behavior.
- Intervention policy: whether a person may touch the robot, provide a new command, reset the scene, teleoperate a motion or approve a recovery.
- Record: timestamps, failures, retries, pauses, operator actions, collisions, safety stops and maintenance events.
Ask for raw logs or an independently reviewable recording whenever confidentiality permits. If a company supplies only edited video or a single aggregate percentage, treat the evidence as a demonstration rather than a deployment result.
Separate the learned model from the complete robotic system
A robot’s apparent intelligence may come from several layers: cameras and other sensors, calibration, perception software, motion planning, grasping hardware, task logic, safety controls, network services and human assistance. Determine which layer produced each result.
- Ask whether the behavior was generated by a learned policy, a scripted routine, a planner, or a combination.
- Identify which actions were autonomous and which required a remote operator, human reset or verbal confirmation.
- Ask what assumptions were built into the environment, such as fixed object locations, fiducial markers, restricted traffic or specially prepared workpieces.
- Request the latency and failure behavior of the full system, not only the neural model’s inference time.
This distinction prevents a strong perception model or carefully engineered cell from being mistaken for a general-purpose autonomous capability.
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Reliability is the first practical test. Epoch AI’s assessment of autonomous robotics, published February 10, 2026 by Yann Rivière and Jean-Stanislas Denain, evaluates reliability and failure modes alongside speed, cost, operating constraints and transfer. The authors caution that an apparently solved task can remain brittle in deployment, writing: “Impressive demos are not hard to come by in autonomous robotics.”
Run consecutive trials under a predeclared protocol. Do not let the vendor choose only successful attempts or silently retry until a favorable outcome appears.
Define the denominator
Success rates are meaningless unless the denominator is clear. Record every scheduled attempt, including timeouts, collisions, dropped objects, aborted motions and safety stops. State whether a retry after a failed grasp counts as one failed trial or as a successful task with extra attempts. Both figures can be useful, but they answer different questions.
| Metric | What to record | Why it matters |
|---|---|---|
| First-attempt success | Completed tasks without retry, reset or human help | Shows how often the system works as initially presented |
| Eventual task success | Completion after permitted autonomous retries | Shows recovery ability and the time cost of failures |
| Failure rate by mode | Perception, grasp, planning, collision, software, hardware and safety failures | Reveals whether one dangerous or expensive mode is hidden by an average |
| Intervention rate | Human touches, teleoperation, scene resets and remote approvals | Measures labor that may be omitted from an autonomy claim |
| Time per completed task | Median and distribution, including recovery and waiting | Connects capability to throughput and staffing |
Capture failure modes, not just percentages
Require a failure taxonomy before testing. Examples include confusing two similar objects, losing a grasp, entering a forbidden workspace, timing out while searching, repeating an unsafe motion, stopping without a clear reason, or requiring a person to clear an object. For every failure, record the scene, robot state, attempted recovery, damage or hazard, and time to resume.
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A high aggregate success rate can conceal a low-frequency event that damages products or injures a nearby worker. Conversely, a recoverable misgrasp may be acceptable in a low-cost process if the robot detects it and resumes without intervention. The decision depends on the target operation, so use the failure record rather than a universal threshold.
Test transfer instead of assuming generalization
Performance on the exact objects and layouts used during tuning is in-distribution performance. It does not establish that the system will handle a new object, a changed shelf position or a new sequence of familiar skills. Epoch AI notes that transfer is rarely demonstrated and should not be inferred from task-specific fine-tuning.
Use a matrix that changes one axis at a time and then combines changes. MESA’s evaluation is a useful model because it reports in-distribution performance plus four distinct transfer axes.
| Test axis | Evaluator question | Example change |
|---|---|---|
| In-distribution | Does the system perform conditions represented in its evaluation set? | Objects, positions and instructions drawn from the tested set |
| Spatial configuration | Does it identify and manipulate the target when positions or layouts change? | Move bins, alter shelf spacing or reverse the approach direction |
| Object instance | Can it handle a new instance of a familiar object? | Different package, color shade, wear pattern or supplier |
| Object category | Can it handle a category absent from tested training conditions? | Introduce a new household or industrial item with similar affordances |
| Subtask composition | Can it combine known skills into a new sequence? | Pick, inspect, orient and place rather than only pick and place |
| Embodiment or hardware | Does the capability persist on a different robot or configuration? | Change arm reach, gripper, camera position or compute unit |
Keep the holdout set private from the tuning team when possible. Randomize object order and location, and disclose how much additional data or engineering was used after each change. A model that needs a new training run for every object instance may still be commercially useful, but it should not be presented as broadly general.
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Check whether performance fits the real operation
A robot can complete a task and still be unsuitable for production. Compare the complete process with the human or automated baseline it would replace or supplement.
Speed and throughput
Measure cycle time from the first usable observation to a verified completed task. Include perception, planning, motion, retries, safety pauses, operator approvals, network delays and end-of-shift recovery. Report median, slowest decile and downtime rather than a best-case clip.
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Translate cycle time into the units the operation needs: items per hour, orders per shift, pallets per day or inspections per minute. State the number of robots, people and stations required to achieve that rate. A fast arm that waits for a human to confirm every ambiguous scene may deliver less throughput than a slower system that handles routine exceptions autonomously.
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Availability and maintenance
Request scheduled and unscheduled downtime, preventive-maintenance intervals, consumable replacement, calibration frequency and mean time to recover from a fault. Ask which failures require a specialist and whether spare hardware is needed on site. A pilot that runs for a few hours does not establish long-duration uptime.
Operating constraints
Document lighting limits, reflective or transparent materials, dust, temperature, vibration, network dependence, charging time, payload, reach, floor markings and human-traffic rules. Test near the limits the customer actually faces, not only in an optimized cell.
Total cost and human labor
Build a cost model that includes acquisition or lease, integration, installation, software fees, compute, network, maintenance, consumables, facility changes, training, supervision and the value of human interventions. Count remote operators and exception handlers even when they support several robots. Epoch AI treats cost and speed as evaluation dimensions, but it does not establish universal business thresholds; set the threshold for the target process.
Test safety when the system is uncertain or wrong
Success rates do not show whether a robot recognizes that it should not proceed. Google DeepMind’s ASIMOV-Agentic-v1 benchmark evaluates several behaviors that can be converted into customer-specific scenarios:
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- Refusal: declines an instruction that violates operational or safety constraints.
- Protective intervention: stops or enters a safe state during a hardware fault, unexpected obstacle or unsafe human proximity.
- Out-of-distribution shielding: prevents the vision-language-action system from attempting an infeasible or unfamiliar task when confidence is low.
- Help-seeking: asks a qualified person to resolve ambiguous instructions or scenes instead of guessing.
Design tests with realistic consequences. Place a permitted object beside a prohibited one, introduce a person into the work envelope, remove a required fixture, provide contradictory instructions, interrupt a sensor, or present an unfamiliar object. Define the safe response before the trial: stop location, torque or speed limit, alert path, human clearance procedure and restart authorization.
Ask how confidence is estimated and what layer enforces the safety response. A model’s confidence score is not a substitute for an independent safety controller, emergency stop circuit, validated workspace limits and documented operating procedures. Confirm that safety functions remain active when the network, model server or primary sensor fails.
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Determine the maturity of the deployment
Use precise maturity labels rather than treating every live video as production evidence.
| Stage | Evidence expected | What it does not prove |
|---|---|---|
| Research demonstration | One or a few successful behaviors under controlled conditions | Repeatability, economics or safe operation at customer scale |
| Laboratory validation | Repeated tests with documented conditions and failures in a controlled facility | Performance amid real traffic, maintenance and process variation |
| Limited real-world pilot | Consecutive operation in a customer environment with intervention, uptime and incident records | That results transfer to another site or justify full production |
| Production deployment | Proven economic value, domain safety and quality thresholds, support procedures and sustained operation | General capability outside the validated domain |
Epoch AI’s February 2026 assessment reports that autonomous navigation is commercially deployed in several settings, while most industrial and household object manipulation remains in research or controlled applications. It identifies warehouse picking as a clearer commercial manipulation case because the environment can be stabilized and the task is comparatively simple; reliability tends to decline as tasks and settings become more variable. Treat that as a sector-level synthesis, not a guarantee about any particular vendor.
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Use benchmarks as diagnostics, not as purchase decisions
Benchmarks help you compare methods when the test conditions are aligned, but a score is meaningful only with its task definition, hardware, data split, evaluation code and intervention rules. Preserve the benchmark’s scope when quoting a result.
The Robot Benchmark describes work toward common approaches for deciding what to test, how to conduct tests, how to score performance and how to compare results in real-world situations. The site describes this effort as in development, so it should not be presented as an established universal standard.
One published comparison reported by Epoch AI describes 1,800 real-world and 47,000 simulation rollouts in a Toyota Research Institute comparison of a fine-tuned pretrained model with single-task specialists. Epoch says the pretrained model matched or outperformed specialists on nearly all evaluated tasks. This is a secondary account in the 2026 assessment; request the original protocol, task list and intervention rules before using the result to forecast customer performance.
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Simulation volume is not equivalent to field experience. Check the simulator’s contact, friction, sensor, object and failure models, and ask whether the real-world holdout was kept separate from tuning. A high benchmark score can coexist with poor performance on the particular materials, layouts and safety constraints of a customer site.
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Ask how data, adaptation and reproducibility affect the claim
A broad review of robotics foundation models identifies data scarcity, high variability across environments and hardware, uncertainty quantification, safety evaluation, real-time performance and reproducibility as open challenges. Turn each category into a diligence question:
- Data: How many demonstrations, hours of teleoperation, simulated episodes and real failures were required? Who labels or approves the data?
- Adaptation: What must be changed for a new object, camera, gripper, robot or site? Is adaptation a configuration change, fine-tuning run, engineering project or new data collection campaign?
- Uncertainty: What signals indicate low confidence, and are they calibrated against actual failures?
- Real-time behavior: What is end-to-end sensing-to-action latency under production load, including network and safety checks?
- Reproducibility: Can an independent evaluator run the stated version on the stated hardware and obtain comparable results?
- Safety boundary: Which safety functions are independent of the learned policy, and how are updates validated?
Ask what changed between the demonstration and the latest software release. A result obtained with extensive task-specific tuning may be valid, but the required tuning effort belongs in the deployment cost and schedule.
Compare companies on aligned evidence
When two vendors make similar claims, use identical task definitions, holdout objects, layouts, safety rules and intervention policies. Do not compare one vendor’s best-case first-attempt rate with another’s all-trials production log.
| Comparison axis | Evidence to align | Decision question |
|---|---|---|
| Reliability | Consecutive trials, denominator, success definition and failure taxonomy | Which system fails less often, and are its failures tolerable? |
| Transfer | New instances, categories, layouts, task sequences and hardware changes | How much adaptation is needed for the next site or product? |
| Speed and availability | End-to-end cycle time, recovery, downtime and maintenance | Can it meet the required throughput over the required shifts? |
| Total cost and labor | Integration, support, compute, maintenance and intervention hours | Does the complete process beat the relevant baseline? |
| Safety | Refusal, protective stops, uncertainty handling and help requests | Does it fail in a controlled, observable and recoverable way? |
| Maturity | Independent records, pilot duration, incidents and economic evidence | Is the claim research, pilot or sustained production? |
If a cell cannot be established, write “not stated” and preserve the missing evidence as a commercial and technical risk. Do not fill gaps with a benchmark score from a different task.
A practical evaluation procedure
- Define the business task. Specify objects, scene variation, required throughput, acceptable damage, staffing, safety boundaries and the baseline process.
- Freeze the claim. Record robot, software, model, hardware, environment, autonomy level and success definition before the vendor test.
- Request prior evidence. Obtain consecutive-trial logs, failure records, intervention counts, uptime, maintenance and cost assumptions. Mark claims supported only by selected video.
- Build a holdout set. Reserve new object instances, layouts, categories and task combinations that were not used for tuning.
- Run baseline trials. Measure in-distribution performance first, using a predeclared number of attempts and identical reset rules for every supplier.
- Run transfer trials. Change one generalization axis at a time, then combine changes that resemble the customer’s real variability.
- Run safety trials. Introduce ambiguity, obstacles, faults, prohibited objects and low-confidence scenes. Verify refusal, safe stopping, alerting and help-seeking.
- Measure operations. Capture cycle-time distribution, throughput, downtime, recovery time, staffing and all recurring consumables or services.
- Audit interventions. Classify every human touch, remote command, reset, approval and data-collection action. Price the labor and decide whether the intervention is acceptable.
- Review independently. Have a party outside the vendor’s demonstration team inspect logs, replay failures and repeat a sample on the stated hardware.
- Set a go/no-go threshold. Choose task-specific limits for reliability, safety, throughput, availability, cost and adaptation effort. Document who approved each threshold.
- Stage the rollout. Move from controlled pilot to a limited production area with incident reporting, rollback procedures, software-version control and periodic re-evaluation.
Red flags that a demo is carrying too much weight
- The video has no date, software version, trial count or statement of what happened on unsuccessful attempts.
- The company uses “autonomous” while a person is visibly resetting objects, selecting targets, approving actions or taking over remotely.
- Success is defined as a partial motion or a grasp rather than the customer’s completed process.
- Retries, resets and safety stops are omitted from the headline rate.
- The vendor demonstrates only familiar objects and fixed layouts but describes the system as general.
- A benchmark score is quoted without task scope, hardware, data split or intervention policy.
- Simulation rollouts are presented as if they were equivalent to real-world operating hours.
- Safety claims describe a model’s confidence but not an independent protective layer or a tested response to faults.
- Throughput is reported without recovery time, staffing, charging, maintenance or network delays.
- The company cannot state what adaptation is required for a new site, gripper, camera or object category.
- A pilot announcement supplies robot counts but no uptime, incident, quality or economic records.
Make the final decision on evidence, not spectacle
A defensible assessment should let a reader answer six questions: Does the system work repeatedly on the target task? Does it transfer to the variation the business actually has? Is its full-process speed and availability sufficient? Does the economics include human intervention and maintenance? Does it recognize uncertainty and fail safely? Is the evidence mature and independently reviewable enough for the proposed deployment stage?
There is no universal success-rate threshold that makes a robotics system “ready to deploy.” A warehouse picker, mobile navigator and household manipulator face different consequences, variability and cost structures. Set the threshold for the specific task, state how it was measured, and require the company to disclose failures with the same prominence as successes.
The strongest evidence is a repeatable, versioned evaluation in the customer’s conditions, with holdout cases, transparent interventions, safety behavior, operating economics and a plan for monitoring after launch. A polished demo can be the reason to start that evaluation; it should never be the reason to skip it.
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