An open-vocabulary pick-and-drop attempt can fail even when every component looks credible in isolation. OK-Robot combines semantic object retrieval, navigation, grasp selection and robot execution, so the most useful first question after a failed attempt is: which handoff stopped producing a usable input for the next stage? Record that first broken boundary before replacing a model. A final task-failure label alone cannot tell whether the robot went to the wrong object, reached the right area from a poor pose, received an awkward grasp, or could not execute it.
The OK-Robot project describes a modular system tested by its authors on 171 pick-and-drop attempts across 10 homes. Its analysis reports failures in semantic-memory retrieval, difficult manipulation poses and hardware operation among the leading causes. Those are findings about the authors’ trials, not percentages to assign to a new robot or proof that one module is causally independent of the others.
Follow the object request through each handoff
A household instruction names an object and a destination in ordinary language. The system must connect that request to a semantic memory of a scanned home, choose where to navigate, reach a manipulation pose, produce a grasp and complete the drop. OK-Robot’s public repository exposes navigation, manipulation and hardware components, alongside setup steps for environment scanning and a workstation/robot connection. A useful failure log should therefore retain the request and the output of each stage, not only the final camera clip. The paper identifies static semantic memory and obstacle maps as a limitation: a moved object can make an old scan misleading. Record scan age and changed objects before blaming the live grasp module.
The first-failure label is relative to a declared acceptance test. If retrieval selected the wrong mug, later navigation to that mug can be technically successful while the task is already lost. If retrieval was correct but navigation stopped outside reach, a grasp failure is downstream evidence, not necessarily the first cause. If the robot reached the right object with a feasible pose but the gripper failed, the hardware/action boundary deserves attention. This approach makes the debugging question narrower than generic post-action verification.
| Boundary | Retain from the attempted run | Proposed acceptance question |
|---|---|---|
| Request to semantic memory | Query, selected object instance, confidence/context and scan version | Was the intended physical object selected? |
| Memory to navigation | Goal location, path and final base pose | Did the robot arrive where manipulation was feasible? |
| Navigation to grasp proposal | Object observation, pose proposal and collision context | Was the grasp target reachable and appropriate? |
| Grasp to hardware execution | Commanded motion, robot state and gripper outcome | Did the machine execute the planned contact and lift? |
| Carried object to drop | Destination selection and release evidence | Did the requested object reach the intended place? |
This is an editorial logging matrix, not a claim that the OK-Robot paper used these exact fields or acceptance criteria. Some stages overlap in practice; a slightly wrong base pose may make a valid grasp proposal infeasible, so the log should preserve both facts.
Replay one boundary without erasing the upstream record
Imagine a hypothetical request to move a blue cup from a shelf to a table. The robot enters the correct room but approaches a similar blue container instead. A proposed investigation would inspect the semantic-memory match before changing navigation or grasping. Replay the same selected object and goal through navigation to see whether that module can reach it; then substitute a verified target object and rerun the downstream stages. Keep the original failure and the intervention separately. If a substituted target leads to success, it strengthens a retrieval hypothesis; it does not prove retrieval was the only cause in every natural run.
For another attempt, suppose the right cup is selected but the robot stops too far from the shelf. Check the planned goal and final base pose before labeling the grasp model defective. The mobile-robot planner/controller guide describes the adjacent navigation distinction; OK-Robot’s diagnostic job is to locate that boundary inside a complete household task. If the base pose is acceptable but the grasp pose is difficult, retain the actual camera/depth view and proposed grasp before replacing the perception model. No blue-cup episode or replay was performed for this article.
The EmbodiedSkills planner/verifier article covers the separate question of checking whether a selected skill succeeded and recovering from an error. A verifier can announce that the cup was not delivered; it does not automatically identify the first failed interface between semantic retrieval, navigation, manipulation and hardware. OK-Robot motivates logging those interfaces because a modular pipeline can compound small upstream errors.
Bound the result and the release dependencies
The project website reports a 58.5% overall success rate in the authors’ home trials and shows a long tail of failures. That result should stay attached to its 171 attempts, homes, objects and integrated system. A local failure-transition matrix needs fresh trials and explicit labeling; copying the authors’ failure shares would hide a different camera, room layout, robot or grasp stack.
The current OK-Robot README also matters for feasibility. It lists specific hardware and scanning tools, and its installation instructions say to obtain an AnyGrasp license and checkpoint. The OK-Robot repository’s MIT license does not imply that every external model or dependency is distributed under MIT; the roadmap explicitly discusses replacing grasp perception so all dependencies could be MIT licensed. Check each required component’s terms and availability before planning a reproduction. None was installed here.
After identifying the first broken boundary, test a single proposed repair with fixed upstream outputs and then rerun complete attempts. The fixed-output replay can localize a defect, while the full run reveals interactions that the replay removed. That is the actionable use of OK-Robot’s modular design. It does not turn a first-failure label into a universal causal explanation or a safety guarantee.