3 papers
cs.RO2026
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Raymond Yu, William Huey, Mustafa Mukadam +2
Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations. Improving these policies with reinforcement learning (RL) is an appealing alternative, b…
cs.RO2025
Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
Chuning Zhu, Raymond Yu, Siyuan Feng +3
Imitation learning has emerged as a promising approach towards building generalist robots. However, scaling imitation learning for large robot foundation models remains challenging…
cs.CV2025
DRAWER: Digital Reconstruction and Articulation With Environment Realism
Hongchi Xia, Entong Su, Marius Memmel +7
Creating virtual digital replicas from real-world data unlocks significant potential across domains like gaming and robotics. In this paper, we present DRAWER, a novel framework th…