From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and
Overview
This research evaluates 281 imitation-learning policies across seven families using approximately 1,700 real-robot trials of RJ45 connector grasping and insertion. The study finds that observation and action representations dominate performance, with specific improvements like region-of-interest cropping and absolute action prediction yielding significant gains. Only five offline metrics reliably correlated with success within a single policy family, indicating that while offline metrics can shortlist policies, real-robot evaluation remains necessary.
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Reported quantity · trials: 1700 other · reported trials
“hoices that govern real-world performance, 281 imitation-learning policies spanning seven policy families were evaluated in approximately 1,700 real-robot trials of RJ45 connector grasping and insertion using a five-fingered robotic hand. Controlled ablations showed that observation and action representations dominated conventional optimizer and sampling choices. For point-cloud policies, region-of-interest cropping,”
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Reported quantity · offline_metrics_evaluated: 67 other · Basis not reported
“r effects were not independently isolated. Of 67 offline metrics evaluated, only five measures of predicted-motion smoothness and directness reliably correlated with insertion success (r = -0.70 to -0.80), and only within a single policy family. These results identify representation choices as key determinants of real-world imitation-learning performance and show that offline metrics can shortlist policies, but canno”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalFrom Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation
This research evaluates 281 imitation-learning policies across seven families using approximately 1,700 real-robot trials of RJ45 connector grasping and insertion. The study finds that observation and action representations dominate performance, with specific improvements like region-of-interest cropping and absolute action prediction yielding significant gains. Only five offline metrics reliably correlated with success within a single policy family, indicating that while offline metrics can shortlist policies, real-robot evaluation remains necessary.
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