3 papers
cs.RO2026
eRLT: Efficient VLA Reinforcement Learning via Action-Relevant Token Routing
Dehao Huang, Jianbang Liu, Jianpan Gao +7
Vision-Language-Action (VLA) models provide strong behavioral priors for robotic manipulation, yet efficiently adapting them to downstream tasks remains challenging. Recent work ad…
cs.RO2026
What Matters in Designing World Action Models: An Empirical Study
Chao Tang, Haoqing Wang, Zilang Cen +9
World Action Models (WAMs) have emerged as a promising paradigm for generalizable robot control. Despite the growing number of WAM systems, existing works often introduce unified s…
cs.RO2026
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
Dehao Huang, Aoxiang Gu, Chengjie Zhang +5
Task-success confidence estimation for Vision-Language-Action (VLA) models provides a crucial task-level signal for monitoring manipulation in open-world environments and supportin…