Robot failure analysis is the review of failed and borderline robot policy rollouts to work out what caused each failure, where it began, whether the robot recovered, and which problems keep coming back. A success rate tells a team how often a policy fails. Failure analysis tells them why, which is what they need to decide what to change.
A household robot is asked to load a plate into a dish rack. It grasps the plate, lifts it, rotates it, and then the plate ends up on the floor. The rollout gets logged as a failure, and that is where the information stops. The team knows the policy failed, but not why, where the problem began, or what to change.
That gap is what robot failure analysis is meant to close. When we say robot failure here, we mean a robot policy failing during a rollout, not a worn motor or a broken sensor.
This article covers how teams detect, diagnose, and classify robot policy failures, and how they turn those findings into better policies.
- What robot failure analysis is
- Why detecting a failure is not enough
- Common robot policy failure modes
- Failure detection vs. failure diagnosis
- Finding where a failure began
- Failure classification and taxonomies
- Recovery and human interventions
- How failure analysis improves robot policies
- What a failure analysis report should include
What Is Robot Failure Analysis?
Picture a robot that completes most of a task and then drops an object near the end. Or one that repeats the same action until time runs out. Or one that only finishes after a human steps in. All three may show up as failures on a scoreboard, but none of them explains itself.
Robot failure analysis is the systematic examination of failed or borderline robot policy rollouts to identify the cause, the point where the failure began, the recovery behavior, and recurring patterns. It works as a chain of questions: whether something went wrong, why it happened, what type of failure it was, and what the team can learn from it.
This makes failure analysis different from evaluation. Robot policy evaluation tells you how a policy performs across its rollouts. Failure analysis looks more closely at the rollouts that failed or nearly failed to understand what happened and what the team can learn from them. Our post on robot evaluation and benchmarking covers the evaluation side.
The goal is to turn individual failures into information a team can act on.
Why Is Detecting a Robot Failure Not Enough?
A success rate is a useful headline, but a thin one. Two rollouts can both be scored as failures and have nothing in common. Consider three:
- The robot fails to grasp the object at all.
- The robot grasps an object cleanly but picks the wrong one.
- The robot makes an error partway through the task, then gets stuck while trying to recover.
Each points to a different problem. A failed grasp may require changes to perception or manipulation. Selecting the wrong object may point to scene understanding or language grounding. Getting stuck during recovery may require a different policy behavior altogether.
A team that only sees “fail” ends up rewatching hours of video and guessing.
Detailed analysis also shows whether a problem is isolated or recurring. One dropped cup is an anecdote. Twenty rollouts where the failure begins at the same step is a pattern worth investigating.
What Are Common Robot Policy Failure Modes?
A failure mode is a recurring way a robot policy fails during a task. Useful failure modes describe observable behavior or a meaningful cause. “Failed” says little, while “missed grasp” or “collision” gives engineers something they can investigate.
For manipulation and action sequences, practical examples include:
- Missed or failed grasps
- Dropped objects
- Wrong target selection
- Collision or unintended contact
- Getting stuck or timing out
- Failure to recover after an error
- Human intervention
This is a starting point, not a universal list. The failure modes that matter depend on the robot, the task, the environment, and the policy. A navigation policy and a dish-loading policy will not necessarily share the same taxonomy.
How Does Robot Failure Detection Differ From Failure Diagnosis?
Robot failure detection means recognizing that a rollout has failed, or may be heading toward failure. Robot failure diagnosis means working out what caused it.
- Detection: “The robot did not complete the task.”
- Diagnosis: “The robot selected the wrong object after misinterpreting the scene.”
The first tells you to look. The second gives you a direction for investigation.
Research is also treating robot failure analysis as more than a binary pass/fail problem.
REFLECT, a CoRL 2023 paper, explores generating explanations for failed robot executions from summaries of the robot’s experience, with the goal of helping systems understand and correct failures. An ICLR 2025 paper, AHA, similarly investigates detecting and reasoning over failures in robotic manipulation, and frames failure detection as free-form reasoning in natural language.
Diagnosis often means looking at the sequence of actions before the visible failure. The problem may have started earlier than the moment when the robot visibly goes wrong. That is where failure localization becomes important.
How Do You Identify Where a Robot Failure Began?
The last visible error isn’t necessarily where the failure began. A simple rollout shows why:
- The robot perceives the scene.
- It chooses an action.
- It executes the action.
- The action creates an unexpected state.
- Later actions compound the problem.
- The rollout fails.
The failure becomes obvious at step six, but the underlying problem may have started at step two. Failure localization means examining the sequence to identify the first meaningful deviation from the intended behavior.
Consider a robot asked to hand over a red cup. It grasps the blue cup instead, then lifts and delivers it smoothly. The motion itself may look flawless, but the failure began at the object-selection step. The issue points toward perception or language grounding rather than control.
Borderline cases deserve the same attention. A robot that technically completes the task but hesitates, retries, or takes an unusual path may pass today while exposing a policy weakness that causes a failure in a slightly different situation tomorrow.
What Is Robot Failure Classification?
Robot failure classification is the practice of assigning individual failures to meaningful categories. In this context, it refers to the behavior of a robot policy, not to sorting robots by type. The set of categories is a failure taxonomy. Classification is the act of applying that taxonomy to individual rollouts.
A shared taxonomy helps teams:
- Group similar failures.
- Track recurring problems.
- Compare policy versions.
- Spot new failure types.
- Prioritize what to investigate.
Consistency is important. If “missed grasp” means one thing this week and something different next week, comparisons between policy versions become difficult to interpret. Categories need clear definitions and examples.
At the same time, a taxonomy should not become a box that every failure has to fit. Unexpected failures should be flagged rather than forced into the nearest existing category. That gives the taxonomy room to grow as teams encounter new behaviors.
A useful failure taxonomy should be specific enough to make recurring problems comparable, but flexible enough to surface failures the team has not seen before.
What Are the Types of Failure Analysis?
Failure analysis is not one step. It can include detecting that something went wrong, diagnosing the cause, locating where the problem began, classifying the failure, examining recovery behavior, and looking for patterns across multiple rollouts.
Each answers a different question:
- Detection: Did something go wrong?
- Diagnosis: Why did it happen?
- Localization: Where did the failure begin?
- Classification: What type of failure was it?
- Recovery analysis: Did the robot recover, and how?
- Trend analysis: Is this failure recurring across rollouts or policy versions?
Together, these steps provide a more useful picture than a pass/fail label alone.
Why Should Robotics Teams Track Recovery and Human Interventions?
Failure is not always binary. A policy may fail outright, recover on its own, recover after several actions, require a human to step in, or finish the task through an inefficient sequence. Each outcome provides different information about the policy.
Recovery behavior can reveal how robust a policy is when something unexpected happens. A robot that slips and corrects itself is in a different situation from one that repeats the same action until the rollout times out.
Human intervention is also useful data, rather than simply an operational inconvenience. Every time an operator steps in, the policy has revealed a situation it cannot yet handle reliably. Recording when and why interventions happen can help teams identify the limits of a policy.
Near-misses provide another useful signal. A rollout that succeeds despite unstable or undesirable behavior may point to a weakness before it becomes a clear failure.
How Failure Analysis Helps Improve Robot Policies
Failure analysis closes a loop:
- Run policy rollouts.
- Identify failed and borderline outcomes.
- Analyze where each failure began.
- Diagnose and classify the failures.
- Identify recurring failure modes.
- Use those findings to improve the policy.
- Re-evaluate the updated policy.
The point is not a failure report that gets read once and filed. It is to make failure patterns measurable across policy iterations.
For example, a team may discover that a new policy reduces missed grasps but increases wrong-target selections. The overall success rate may show a small improvement, but failure analysis reveals where the remaining problems have shifted.
That is where individual rollouts become engineering feedback. The updated policy can then return to the broader evaluation process for another round of testing and comparison.
What Should a Robot Failure Analysis Report Include?
A useful report stays practical. At a minimum, it should capture:
- Rollout and task information
- Outcome: successful, failed, or borderline
- Failure type and cause
- The point where the failure began
- The relevant sequence of actions
- Recovery behavior
- Human intervention, if any
- Taxonomy category
- Whether the failure is recurring or new
- Possible implications for the policy
With these fields, someone on the team can understand what happened in an individual rollout, while a collection of reports can be filtered and compared across tasks and policy versions.
The goal is to make failure data useful at both levels: detailed enough to investigate individual incidents and structured enough to reveal broader patterns.
How Labelix.ai Analyzes Robot Policy Failures
Labelix.ai’s robot failure analysis service is built around this workflow. A dedicated team reviews failed and borderline rollouts and tags each one with its cause, the point where it began, whether the robot recovered, and when a human had to step in.
The failure taxonomy is developed from the client’s own rollout footage, agreed with their engineers, and documented with definitions and examples. When a failure does not fit an existing category, it is flagged so the taxonomy can grow rather than forcing the event into an inaccurate label.
The resulting analysis can show failure modes by task and model version, along with evaluator agreement on a double-scored sample. This gives robotics teams a structured view of recurring problems and emerging failure types that they can use when improving their policies.
Robotics teams looking to understand why their policies fail can explore Labelix.ai’s robot failure analysis service, or request a pilot on their own rollouts.
Send us your failed rollouts. Get back the reasons.
Start with recent failed and borderline rollouts from one policy. A dedicated Labelix.ai team drafts the failure taxonomy with your engineers, tags every rollout, and shows you the breakdown and the agreement numbers.
FAQs
What is robot failure analysis?
Robot failure analysis reviews failed and borderline policy rollouts to understand why they failed, where the failure began, and whether the robot recovered. Unlike a pass/fail score, it provides information about the cause and behavior behind the outcome.
What causes a robot policy to fail?
Common causes include perception errors, incorrect actions, wrong target selection, execution problems such as collisions or slips, and an inability to recover after a mistake. The cause depends on the task, environment, and policy.
What are robot failure modes?
Robot failure modes are recurring ways a policy fails, such as missed grasps, collisions, wrong target selection, or getting stuck. Categorizing them helps teams identify which problems repeat and prioritize what to investigate across policy versions.
What is the difference between robot failure detection and diagnosis?
Detection identifies that a rollout failed or may be heading toward failure. Diagnosis investigates why, often by reviewing the actions that came before the visible error to identify where the problem began.
How do you classify robot policy failures?
Use a consistent failure taxonomy with clear definitions and examples, based on observable behavior and meaningful causes. Tag rollouts against that taxonomy, while flagging failures that do not fit so new categories can be added when needed.
Cindy writes about Physical AI, robotics, and the human data that teaches models to perceive and act, for Labelix.ai, an independent data foundry for robotics and multimodal AI.
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