2.9k citations · 3.1k across the 32 of their papers we have counts for
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RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Mingxin Yu, Lin Shao, Zhehuan Chen +4
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly sim…
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
Chuanyu Pan, Yanchao Yang, Kaichun Mo +2
We propose a framework to continuously learn object-centric representations for visual learning and understanding. Existing object-centric representations either rely on supervisio…
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
Qi Li, Kaichun Mo, Yanchao Yang +2
Building embodied intelligent agents that can interact with 3D indoor environments has received increasing research attention in recent years. While most works focus on single-obje…
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
Yian Wang, Ruihai Wu, Kaichun Mo +4
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in…
Learning to Regrasp by Learning to Place
Shuo Cheng, Kaichun Mo, Lin Shao
In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses. Regrasping is needed whenever a robot's current gras…
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
Kaichun Mo, Yuzhe Qin, Fanbo Xiang +2
Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics,…