114 citations · 283 across the 18 of their papers we have counts for
18 papers
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment
Bingyang Zhou, Haoyu Zhou, Tianhai Liang +10
We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations. Our dataset consists of around 4400 models covering 11 categories annotated wi…
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
Yichen Li, Kaichun Mo, Yueqi Duan +5
Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focu…
DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations
Qingtao Liu, Yu Cui, Qi Ye +5
Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep reinforcement learni…
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering
Jun Lv, Yunhai Feng, Cheng Zhang +3
Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL. How an accurate model can be developed auto…
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…
SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
Jun Lv, Qiaojun Yu, Lin Shao +3
Building general-purpose robots to perform a diverse range of tasks in a large variety of environments in the physical world at the human level is extremely challenging. It require…
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…
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
Yifan You, Lin Shao, Toki Migimatsu +1
In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability ha…
GRAC: Self-Guided and Self-Regularized Actor-Critic
Lin Shao, Yifan You, Mengyuan Yan +2
Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep…
Generative 3D Part Assembly via Dynamic Graph Learning
Jialei Huang, Guanqi Zhan, Qingnan Fan +5
Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a si…
Design and Control of Roller Grasper V2 for In-Hand Manipulation
Shenli Yuan, Lin Shao, Connor L. Yako +2
The ability to perform in-hand manipulation still remains an unsolved problem; having this capability would allow robots to perform sophisticated tasks requiring repositioning and…
Learning 3D Part Assembly from a Single Image
Yichen Li, Kaichun Mo, Lin Shao +2
Autonomous assembly is a crucial capability for robots in many applications. For this task, several problems such as obstacle avoidance, motion planning, and actuator control have…
Learning to Scaffold the Development of Robotic Manipulation Skills
Lin Shao, Toki Migimatsu, Jeannette Bohg
Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors a…
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
Fabio Ferreira, Lin Shao, Tamim Asfour +1
Endowing robots with human-like physical reasoning abilities remains challenging. We argue that existing methods often disregard spatio-temporal relations and by using Graph Neural…
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
Lin Shao, Fabio Ferreira, Mikael Jorda +6
To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on…
ClusterNet: 3D Instance Segmentation in RGB-D Images
Lin Shao, Ye Tian, Jeannette Bohg
We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of individual objects in…
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
Lin Shao, Parth Shah, Vikranth Dwaracherla +1
Given two consecutive RGB-D images, we propose a model that estimates a dense 3D motion field, also known as scene flow. We take advantage of the fact that in robot manipulation sc…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…