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cs.RO2026
KAM-WM: Kinematic Affordance Maps from Latent World Models for Robot Manipulation
Xinyu Shao, Keru Zhou, Guowei Huang +3
Learning manipulation from few demonstrations requires visual priors that capture not only where to interact, but also how the interaction should begin; static priors such as segme…
cs.RO2026
ResCue: Residual Spatial Cueing for Language-Conditioned Imitation Learning
Yanzhe Tang, Xinyu Shao, Yuxuan Hu +8
Visual imitation policies must learn which image regions matter for action, whether applying the same operation to different objects or selecting a language-specified target. We in…
cs.RO2026
ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation
Zeyuan He, Bowen Yang, Zhirui Fang +9
Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing actions from current observati…