11 citations · 18 across the 13 of their papers we have counts for
9 papers · 1 filter
DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng +27
Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, espe…
PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
Wenlong Huang, Yu-Wei Chao, Arsalan Mousavian +4
Humans anticipate, from a glance and a contemplated action of their bodies, how the 3D world will respond, a capability that is equally vital for robotic manipulation. We introduce…
Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
Junjie Bai, Yu-Wei Chao, Qizhi Chen +13
The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday…
Slot-Level Robotic Placement via Visual Imitation from Single Human Video
Dandan Shan, Kaichun Mo, Wei Yang +4
The majority of modern robot learning methods focus on learning a set of pre-defined tasks with limited or no generalization to new tasks. Extending the robot skillset to novel tas…
MatchMaker: Automated Asset Generation for Robotic Assembly
Yian Wang, Bingjie Tang, Chuang Gan +4
Robotic assembly remains a significant challenge due to complexities in visual perception, functional grasping, contact-rich manipulation, and performing high-precision tasks. Simu…
3D-MVP: 3D Multiview Pretraining for Robotic Manipulation
Shengyi Qian, Kaichun Mo, Valts Blukis +3
Recent works have shown that visual pretraining on egocentric datasets using masked autoencoders (MAE) can improve generalization for downstream robotics tasks. However, these appr…
URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images
Zoey Chen, Aaron Walsman, Marius Memmel +6
Constructing simulation scenes that are both visually and physically realistic is a problem of practical interest in domains ranging from robotics to computer vision. This problem…
STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots
Yi Li, Muru Zhang, Markus Grotz +2
Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses. Here,…
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Chuanruo Ning, Ruihai Wu, Haoran Lu +2
Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulat…