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cs.RO2026
SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control
Bikang Pan, Fan Liu, Haotao Lu +2
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future observations. However, conditioning future prediction only on the task prompt and ob…
cs.RO2026
ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
Yunao Huang, Shiyu Sang, Haotao Lu +5
Contact-rich robot manipulation requires physical interaction cues that are often invisible to cameras, making tactile sensing essential for robust control. However, scaling visuo-…