Motion Smoothness vs. Task Success: A RoboLab-EgoX Failure Case

Motion-smoothness metrics can help prioritize robot-data recordings for review but cannot determine whether a robot completed its task, as shown by an analysis of 127 simulated RoboLab-EgoX cube-placement episodes. Using FiftyOne’s open-source demo_quality_scorer plugin, the author measured joint and end-effector motion with metrics including spectral arc length, log dimensionless jerk, jerk RMS, and frequency-power ratios, finding that successful episodes were generally smoother than failures overall. However, one failed rollout, RubiksCubeTask_cape_hill_sf0, ranked eighth smoothest, triggered no metric warnings, and was not flagged as a statistical outlier, demonstrating that a fluid trajectory can still place an object incorrectly. The analysis used manually configured two-second windows because the approximately 5.4-second episodes were too short for the plugin’s standard windowing approach, and it emphasizes that scores are relative to the evaluated batch rather than universal quality measures. The article concludes that motion and sensor-quality scoring is valuable for triage, particularly for identifying issues such as sensor dropouts or rough execution, but should supplement rather than replace task-success labels and human inspection.

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