Time-Indexed Skill Success Models for Latency-Aware Skill Selection and Timing in Dynamic Manipulation
Overview
This paper presents time-indexed skill success models designed to predict task success when skills are launched after a specific delay, addressing the challenge of outdated observations in dynamic manipulation.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · percentage points improvement: 2.8 other · Basis not reported
“r a given delay. Trained on rollouts branched at a grid of launch delays, they let a receding-horizon planner select both the skill and its launch time under fixed or random latency. In a MuJoCo study in which a UR5e captures a sliding puck with three skills, the planner exceeded a static competence model by 2.8 to 6.2 percentage points at 100 to 300 ms of latency, including when that model was evaluated on point or”
Exact source · revision 1Source owner not reported
Reported quantity · expected calibration error: 0.009 other · Basis not reported · Differing source assertions
“sampled forecasts of the launch state. Across five complete runs (three training seeds and two distribution shifts), all 80 paired 95% intervals against four baselines excluded zero. On delayed launches, the time-indexed model was calibrated (expected calibration error 0.009 against 0.239), and pausing the world during latency overstated success by up to 23 points. On a real UR5e that captures a rolling ball after 0.”
Exact source · revision 1Source owner not reported
Reported quantity · expected calibration error: 0.239 other · Basis not reported · Differing source assertions
“sampled forecasts of the launch state. Across five complete runs (three training seeds and two distribution shifts), all 80 paired 95% intervals against four baselines excluded zero. On delayed launches, the time-indexed model was calibrated (expected calibration error 0.009 against 0.239), and pausing the world during latency overstated success by up to 23 points. On a real UR5e that captures a rolling ball after 0.”
Exact source · revision 1Source owner not reported
Reported quantity · overstated success points: 23 other · Basis not reported
“sampled forecasts of the launch state. Across five complete runs (three training seeds and two distribution shifts), all 80 paired 95% intervals against four baselines excluded zero. On delayed launches, the time-indexed model was calibrated (expected calibration error 0.009 against 0.239), and pausing the world during latency overstated success by up to 23 points. On a real UR5e that captures a rolling ball after 0.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalTime-Indexed Skill Success Models for Latency-Aware Skill Selection and Timing in Dynamic Manipulation
This paper presents time-indexed skill success models designed to predict task success when skills are launched after a specific delay, addressing the challenge of outdated observations in dynamic manipulation.
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