Tag
robotics
Robotics here focuses on the methods that let machines act reliably in the physical world: perception, control, simulation, and planning. Topics include world models, hierarchical planning, sparse-demonstration learning, and deformable-object simulation, all aimed at better long-horizon control with less compute.
2 articles
Research/Apr 10
SIM1 turns sparse demos into deformable-world data
SIM1 grounds deformable-object simulation in real scenes, then scales sparse demos into synthetic training data for data-efficient robot policy learning.
Research/Apr 6
Hierarchical Planning Cuts World-Model Search Cost
A hierarchical latent world-model planner improves long-horizon control and cuts planning compute, with zero-shot gains on real robots.