Utility-WAM: Correcting Future-Induced Action Changes
Overview
This research paper examines how predictive future contexts affect robot action execution, identifying a failure mode called 'future-induced negative transfer' that harms precision tasks. The paper introduces Utility-WAM, a framework that corrects these issues using a residual adapter, improving success rates and reducing inference latency across multiple benchmarks.
Generated from attributed reports · Updated 7 minutes ago
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 · benchmark success points: 0.8 other · Basis not reported · Differing source assertions
“hifts average benchmark success ($+0.8$ points on LIBERO, $-0.8$ on RoboTwin~2.0), while generating visual futures increases inference latency by 1.7--1.9$\times$. These aggregate metrics conceal a concentrated failure mode that we term \emph{future-induced negative transfer}: while predictive deltas benefit long-horizon tasks ($+2.4$ points on LIBERO-Long) and remain negligible on most routine manipulations, they in”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency multiplier: 1.7 other · Basis not reported · Differing source assertions
“hifts average benchmark success ($+0.8$ points on LIBERO, $-0.8$ on RoboTwin~2.0), while generating visual futures increases inference latency by 1.7--1.9$\times$. These aggregate metrics conceal a concentrated failure mode that we term \emph{future-induced negative transfer}: while predictive deltas benefit long-horizon tasks ($+2.4$ points on LIBERO-Long) and remain negligible on most routine manipulations, they in”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency multiplier: 1.9 other · Basis not reported · Differing source assertions
“hifts average benchmark success ($+0.8$ points on LIBERO, $-0.8$ on RoboTwin~2.0), while generating visual futures increases inference latency by 1.7--1.9$\times$. These aggregate metrics conceal a concentrated failure mode that we term \emph{future-induced negative transfer}: while predictive deltas benefit long-horizon tasks ($+2.4$ points on LIBERO-Long) and remain negligible on most routine manipulations, they in”
Exact source · revision 1Source owner not reported
Reported quantity · benchmark success points: 2.4 other · Basis not reported · Differing source assertions
“hifts average benchmark success ($+0.8$ points on LIBERO, $-0.8$ on RoboTwin~2.0), while generating visual futures increases inference latency by 1.7--1.9$\times$. These aggregate metrics conceal a concentrated failure mode that we term \emph{future-induced negative transfer}: while predictive deltas benefit long-horizon tasks ($+2.4$ points on LIBERO-Long) and remain negligible on most routine manipulations, they in”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-11-01T15:21:17.254Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalUtility-WAM: Correcting Future-Induced Action Changes
This research paper examines how predictive future contexts affect robot action execution, identifying a failure mode called 'future-induced negative transfer' that harms precision tasks. The paper introduces Utility-WAM, a framework that corrects these issues using a residual adapter, improving success rates and reducing inference latency across multiple benchmarks.
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