4 citations · 5 across the 15 of their papers we have counts for
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cs.RO2026
DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling
Seongheon Park, Heecheol Kim, Shulin Tian +5
Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robotic data. Verifier-guided test…
cs.RO2024★ 4 cited
AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
Jiafei Duan, Wilbert Pumacay, Nishanth Kumar +7
Robotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models…