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cs.RO2026
-WAM: Repair-and-Reject Post-Training for World Action Models
Ruiyan Xu, Haisheng Su, Sixu Lin +4
World Action Models (WAMs) emerge as a promising foundation for policy refinement by predicting the consequences of sampled actions. However, visually plausible predictions can mis…
cs.RO2026
Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation
Tao Sun, Beining Han, Patrick Yin +5
Rebar insertion is among the most repetitive and physically demanding tasks on construction sites, and a contact-rich problem at 1.4 mm clearance. The parts, however, vary at two l…
cs.RO2026
WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning
Haisheng Su, Zongdai Liu, Xin Jin +13
World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constraine…