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Computer Vision Papers·2026
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Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience

Tianqi Liu, Hanfei Zhao +13·Nanyang Technological University, Ropedia +3

Predicts future egocentric video from actions for human and robot embodiments. It represents movement with camera poses and manipulation with projected skeletons, trained on a unified collection of human and robot experience.

#paper#embodied-ai#robotics#world-model#foundation-model+5
Computer Vision Papers·2026
Icon for item

LaLa-World: Scaling Latent World Models with Natural Language Actions

Junha Song, Geonmo Gu +6·Brown University, Georgia Institute of Technology +2

Uses natural language to describe actions and visual changes, letting one predictor learn from simulation, robot trajectories, images, and videos. It supports cross-domain planning and can initialize robot policies.

#paper#robotics#embodied-ai#world-model#vision+1
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