8 papers
Refining Compositional Diffusion for Reliable Long-Horizon Planning
Kyowoon Lee, Yunhao Luo, Anh Tong +1
Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However, when local plan di…
Compositional Visual Planning via Inference-Time Diffusion Scaling
Yixin Zhang, Yunhao Luo, Utkarsh Aashu Mishra +3
Diffusion models excel at short-horizon robot planning, yet scaling them to long-horizon tasks remains challenging due to computational constraints and limited training data. Exist…
Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Wule Mao, Zhouheng Li, Yunhao Luo +2
Achieving safe, efficient, and kinematically feasible planning in dynamic environments remains a significant challenge, as planners must simultaneously handle moving obstacles, sen…
Generative Trajectory Stitching through Diffusion Composition
Yunhao Luo, Utkarsh A. Mishra, Yilun Du +1
Effective trajectory stitching for long-horizon planning is a significant challenge in robotic decision-making. While diffusion models have shown promise in planning, they are limi…
Grounding Video Models to Actions through Goal Conditioned Exploration
Yunhao Luo, Yilun Du
Large video models, pretrained on massive amounts of Internet video, provide a rich source of physical knowledge about the dynamics and motions of objects and tasks. However, video…
Potential Based Diffusion Motion Planning
Yunhao Luo, Chen Sun, Joshua B. Tenenbaum +1
Effective motion planning in high dimensional spaces is a long-standing open problem in robotics. One class of traditional motion planning algorithms corresponds to potential-based…
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data
Daoan Zhang, Yunhao Luo, Jianguo Zhang
We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We first figure out th…
Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation
Xu Zheng, Yunhao Luo, Pengyuan Zhou +1
In this paper, we tackle a new problem: how to transfer knowledge from the pre-trained cumbersome yet well-performed CNN-based model to learn a compact Vision Transformer (ViT)-bas…