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Evaluate a robot foundation model as a complete deployed system, not as a single success-rate number. A credible evaluation measures task completion and quality, performance on unseen objects and environments, safety around people, recovery after disturbances, real-time latency, hardware resource use, and reproducibility. It also records every intervention, reset, near miss, and failure. Simulation is useful for screening and regression tests, but physical-robot trials establish whether a model is ready for the intended workplace.
Start with the deployment claim
Before running an episode, write down exactly what you are claiming. “The model can manipulate objects” is too broad to test. A defensible claim identifies the robot embodiment, sensors, action space, controller, model checkpoint, prompt format, fine-tuning data, operator permissions, and target environment.
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Specify the system under test
- Embodiment: robot arm or mobile manipulator, degrees of freedom, gripper, payload and any compliance or force control.
- Sensors: camera locations, depth or tactile sensors, frame rates, calibration procedure and whether images are compressed or filtered.
- Actions: end-effector poses, joint targets, velocity commands, discrete skills or another interface, including command frequency and safety limits.
- Model: checkpoint identifier, inference settings, context window, prompt template, tool calls and any external planner.
- Training: pre-training and fine-tuning data, demonstrations, synthetic data and whether test objects or scenes appeared in training.
- Environment: room layout, lighting, surfaces, object set, people present and allowed workspace boundaries.
- Human involvement: whether an operator may pause, teleoperate, remove an obstacle, edit a prompt or reset the scene.
Separate zero-shot, few-shot and fine-tuned results. A few demonstrations supplied at test time are part of the method, not an incidental detail. If an operator can intervene, report that policy and count every intervention; otherwise a reader cannot distinguish autonomous capability from human assistance.
Define pass and fail before testing
Write the task completion condition, safety constraints, time limit, termination rules and reset procedure before collecting data. Decide whether a dropped object, a wrong placement, a collision, a person entering the workspace or a missed control deadline ends the episode. Pre-register episode counts, randomization, stopping rules and exclusion criteria. Do not remove an inconvenient run after seeing its outcome unless the pre-registered rule permits it.
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Build a task matrix that exposes generalization
A nominal demonstration can show that a model memorized one scene. Generalization requires held-out combinations that preserve the task objective while changing what the model must perceive or plan.
Task dimensions to include
| Dimension | Examples | What it tests |
|---|---|---|
| Nominal tasks | Pick, place, sort or open an object in a familiar layout | Basic capability and calibration |
| Compositional tasks | Find an item, move an obstacle, grasp it and place it in a specified container | Multi-step planning and sequencing |
| Unseen objects | New shapes, textures, sizes or packaging not present in training | Visual and physical transfer |
| Pose variation | Object rotated, partially hidden or placed at a new height | Geometric robustness |
| Language variation | Paraphrases, ambiguous references and different levels of detail | Instruction grounding |
| Scene variation | New room, camera position, lighting, clutter and distractors | Environment transfer |
| Physics variation | Different friction, surface compliance, object weight or grasp resistance | Contact and control adaptation |
| Disturbance and recovery | Object displacement, blocked path, occlusion or a temporary sensor dropout | Detection, safe stopping and resumption |
Hold out objects, rooms and task variants at the level of the test split. Merely changing a random seed while retaining the same objects and layout does not establish broad generalization. Keep a separate nominal set so that robustness results can be interpreted against a known baseline.
The Colosseum project illustrates why this matters: across the perturbation factors it tested, success rates degraded by 30–50%. The exact percentage is benchmark-specific, but the practical lesson is general: report the degradation caused by each perturbation rather than only an aggregate score.
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Physical episodes are expensive, but a handful of cherry-picked demonstrations cannot support a deployment decision. Use a repeatable protocol that preserves the denominator.
- Randomize according to the task definition. Generate object poses, language variants and scene conditions from a documented distribution. Keep a locked held-out set for final evaluation.
- Calibrate and verify. Check camera extrinsics, force or torque readings, workspace limits, emergency stops and clock synchronization before the first episode and after any hardware change.
- Execute without selective restarts. Start the timer and logging before the model receives the observation. A failed grasp, aborted run or safety stop remains an episode.
- Log the full trace. Store observations, model outputs, commands, timestamps, controller status, safety events, operator actions, resets and termination reason.
- Apply the pre-set stopping rule. Do not extend an episode indefinitely because a model appears close to success. Record the time limit and whether the model was still moving when it expired.
- Report uncertainty. Publish the number of episodes, successes and failures, plus an uncertainty interval around each estimate. Include the denominator for every subgroup, not just the overall total.
Report results by task, object, environment and perturbation, as well as the aggregate. An average can conceal a model that succeeds on easy scenes and fails consistently on a safety-critical subset.
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Measure more than completion
Binary success is necessary, but it collapses important differences. A model that completes a task after five retries, scrapes a person’s workspace and takes ten minutes should not receive the same operational assessment as one that completes it cleanly and quickly.
| Metric | Definition to publish | Why it matters |
|---|---|---|
| Task success | Completed episodes divided by all valid episodes, with the denominator and termination rules | Basic outcome, but sensitive to task definition |
| Graded quality | Distance from the desired placement, force profile, order correctness or other task-specific score | Distinguishes barely acceptable from high-quality completion |
| Time to completion | Elapsed time from first observation to successful termination, including planning and control | Shows throughput and deadline risk |
| Retries | Additional grasps, approaches or subtask attempts per episode | Captures inefficiency hidden by eventual success |
| Recovery success | Fraction of induced disturbances detected and resolved without a reset | Measures resilience in changing scenes |
| Intervention rate | Episodes or seconds requiring an operator, with intervention type | Separates autonomy from supervision burden |
| Collision and near miss | Contact with people, forbidden surfaces or obstacles, plus near misses under a defined distance threshold | Reveals unsafe behavior before damage occurs |
| Damage and reset events | Broken, spilled or displaced items and hardware or software resets | Connects model behavior to operational cost |
| Safe success | Task completion only when all safety constraints are satisfied | Prevents unsafe completion from counting as success |
Define a safe-success metric as a joint condition: the task is complete and no prohibited safety event occurred. ResponsibleRobotBench applies this principle to 23 multi-stage hazardous tasks. In its reported evaluation, safe scores were 0.64 for GPT-4o, 0.40 for GPT-4o mini and 0.35 for Qwen-72B. Those values belong to that benchmark’s tasks, hardware and protocol; they are not universal rankings of the models.
Evaluate safety around people
Safety testing must include both what the robot did and what it was about to do. Count collisions, near misses, force-limit violations, entry into exclusion zones, dropped loads and unsafe stops. Record whether a person was exposed, how close the robot came, the robot’s speed and the severity of the potential outcome.
Use severity-weighted reporting
- Classify events by consequence, such as harmless contact, property damage, possible injury and serious injury exposure.
- Report event counts and rates per episode or operating hour, not only a narrative description.
- Record the first unsafe command, the sensor evidence available to the model and the protective response.
- Test safe stopping when a person or obstacle appears unexpectedly, including whether the robot remains stopped until the scene is verified.
- Do not allow an operator to silently correct a dangerous trajectory; log the intervention and treat it as a safety event.
Confidence expressed by a model is not a safety guarantee. Include examples where the model appeared certain but failed, and examples where it appropriately declined, stopped or requested help.
Test robustness and recovery
Introduce one controlled disturbance at a time before combining several. Useful perturbations include moving an object after planning, covering part of the view, changing illumination, dropping a camera frame, introducing a language ambiguity or blocking the planned route.
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Score the recovery sequence
- Detection: Did the system notice that its assumptions were no longer valid?
- Protection: Did it slow, stop or retreat before causing harm?
- Diagnosis: Did it identify the relevant cause, such as occlusion or displacement, rather than hallucinating progress?
- Recovery: Did it choose a valid new action and complete the task?
- Resumption: Did it continue without hidden operator help or a full scene reset?
- Cost: How much extra time, motion, energy and intervention did recovery require?
Report recovery time and failure mode separately. A system that always stops safely but never resumes is safer than one that continues unsafely, yet it has a different operational value. Your report should make that trade-off visible.
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Real-time behavior is part of capability. A model that produces an excellent plan but misses control deadlines cannot perform reliably on a physical robot. Firoozi et al. (2023) identify real-time execution, scarce robot data, safety guarantees and uncertainty quantification as major open challenges; an Advanced Robotics review in 2024 likewise highlights latency as a deployment barrier.
Log the complete timing path
- Sensor exposure, capture and transfer time.
- Pre-processing and observation assembly time.
- Model inference time, including token generation or action decoding.
- Planner, controller and safety-filter time.
- Network round-trip delay and jitter when inference is remote.
- Actuator command and acknowledgement time.
- Control frequency, dropped frames, deadline misses and queue buildup.
Report median and tail latency, not only an average. A rare long pause can be the event that causes a collision. State the exact CPU, GPU or accelerator, memory, operating system, software versions, numerical precision, quantization, communication stack and thermal conditions. Log CPU and GPU utilization, memory, energy draw and thermal throttling. Repeat the measurement after sustained operation because heat can reduce throughput.
Compare models only on identical hardware and software settings. If one model uses a faster precision mode or a different communication path, disclose it as part of the system configuration rather than attributing the difference solely to model architecture.
Use simulation as a screening stage
Simulation is valuable for inexpensive regression tests, broad perturbation sweeps and controlled ablations. It lets you vary object poses, lighting, friction and sensor faults while keeping the task definition fixed. It does not, by itself, establish safe physical performance.
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- Implement the same task goals, success conditions, time limits and safety constraints in simulation and on the robot.
- Run a fixed simulation suite on every checkpoint to catch regressions before consuming hardware time.
- Use simulation to identify brittle conditions and select representative physical tests, rather than replacing those tests.
- Promote a checkpoint to physical evaluation only after it passes the pre-defined simulation gate.
- Label simulation and real-robot evidence in separate tables and plots; never pool them into one success rate.
- Compare predicted and observed failure modes. A large mismatch is evidence of a simulation-to-real gap that needs investigation.
When reporting transfer, state which visual, physical and control properties were randomized and which were not. A model may transfer across camera noise while failing under a small friction change, so a single “sim-to-real” number is not informative.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check transfer across robots and sites
If the intended claim covers more than one robot or facility, test more than one embodiment or clearly narrow the claim. Differences in reach, gripper geometry, camera placement, calibration, controller and workspace can invalidate an apparent transfer.
Use one protocol for all sites and publish site-specific results. Record which components were changed, whether prompts or demonstrations were adapted, and how much engineering or calibration time was required. RoboArena authors describe comprehensive, unbiased and comparable evaluation of generalist policies as uniquely challenging and use distributed real-world evaluation to address scale. That framing makes the reporting requirement clear: a broad claim needs evidence from multiple tasks and environments, not a larger sample from one convenient setup.
Compare two models without creating a false ranking
Only compare scores when the task definitions, robot embodiment, prompts, data access, hardware, intervention rules and stopping criteria are aligned. Otherwise, present the results as separate case studies.
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|---|---|
| Nominal capability | What is the success and quality on the same baseline tasks? |
| Held-out generalization | How do unseen objects, rooms, poses and language variants affect performance? |
| Safety | What are safe-success rates, near misses, collisions and severity-weighted failures? |
| Recovery | Can each model detect disturbances, stop safely and resume without intervention? |
| Real-time performance | What are tail latency, control frequency, deadline misses and throughput on identical hardware? |
| Data requirement | How many demonstrations or fine-tuning examples are needed, and what is held out? |
| Cross-robot transfer | Does performance persist after changing embodiment, calibration or site? |
| Reproducibility | Are checkpoint, prompts, software, randomization, logs and denominators disclosed? |
| Operating cost | What compute, energy, supervision and reset burden is required per successful task? |
Do not rank models from incompatible benchmarks. A higher nominal score can be less useful if it requires more operator interventions, misses deadlines or produces more severe failures.
Publish a failure record readers can audit
Release representative videos or traces for successes, ordinary failures, unsafe events and recovery attempts. Include the model’s observation, action, timing, confidence or verbal rationale when available, and the human response. List intervention and reset counts, not just the final score.
For every aggregate metric, provide the episode denominator, uncertainty interval, task mix and termination reasons. State exclusions explicitly. If a sensor failed, distinguish a hardware fault from a model failure, but do not silently remove the episode; show both the operational result and the diagnostic analysis.
A 2026 user study by Sheidlower and colleagues found that people value task-success rates together with failure cases, videos and estimates for novel tasks. That is a practical reporting standard: readers need evidence of what happens outside the headline average.
A practical go/no-go checklist
- The deployment claim names embodiment, sensors, actions, checkpoint, prompt, data and operator policy.
- Nominal, compositional, held-out and perturbation tasks are defined before testing.
- Objects, rooms and task variants are held out to measure generalization.
- Every episode, abort, safety stop, intervention and reset is counted.
- Success is accompanied by quality, time, retries, recovery, near-miss and damage metrics.
- Safe success requires completion and compliance with safety constraints.
- Latency tails, deadline misses, resource use and thermal behavior are measured on target hardware.
- Simulation results are labeled separately and checked against physical trials.
- Cross-robot or cross-site evidence supports any claim broader than one setup.
- Failure traces, denominators, uncertainty and termination reasons are available for review.
A model is ready for a real-world task only when it meets the task’s success and safety gates under the stated intervention policy, stays within real-time limits on the deployed hardware, and demonstrates acceptable behavior on held-out conditions. If any of those conditions is unknown, the correct conclusion is not that the model is capable; it is that the evaluation is incomplete.
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