117 citations · 327 across the 26 of their papers we have counts for
26 papers
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Jingbang Chen, Yian Wang, Xingwei Qu +4
Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of parti…
Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation
Hongcheng Wang, Yuxuan Wang, Fangwei Zhong +4
Visual-audio navigation (VAN) is attracting more and more attention from the robotic community due to its broad applications, \emph{e.g.}, household robots and rescue robots. In th…
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Zihan Ding, Yuanpei Chen, Allen Z. Ren +4
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands. Scripting policies from scratch is intractable due to t…
PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations
Haoran Geng, Ziming Li, Yiran Geng +3
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categor…
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation
Ruihai Wu, Chuanruo Ning, Hao Dong
Understanding and manipulating deformable objects (e.g., ropes and fabrics) is an essential yet challenging task with broad applications. Difficulties come from complex states and…
GFPose: Learning 3D Human Pose Prior with Gradient Fields
Hai Ci, Mingdong Wu, Wentao Zhu +4
Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the co…
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao +6
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible d…
End-to-End Affordance Learning for Robotic Manipulation
Yiran Geng, Boshi An, Haoran Geng +3
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can…
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification
Mingdong Wu, Fangwei Zhong, Yulong Xia +1
Object Rearrangement is to move objects from an initial state to a goal state. Here, we focus on a more practical setting in object rearrangement, i.e., rearranging objects from sh…
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
Yan Zhao, Ruihai Wu, Zhehuan Chen +4
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments. Towards building scalable systems that…
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Yuanpei Chen, Tianhao Wu, Shengjie Wang +8
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through re…
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
Tianhao Wu, Fangwei Zhong, Yiran Geng +4
Grasping moving objects, such as goods on a belt or living animals, is an important but challenging task in robotics. Conventional approaches rely on a set of manually defined obje…
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…
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…
Fast and Flexible Human Pose Estimation with HyperPose
Yixiao Guo, Jiawei Liu, Guo Li +2
Estimating human pose is an important yet challenging task in multimedia applications. Existing pose estimation libraries target reproducing standard pose estimation algorithms. Wh…
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Ruihai Wu, Yan Zhao, Kaichun Mo +7
Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in human environments is an important yet challenging task for future home-assistant robots. The space of…
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
Jie Ren, Yewen Li, Zihan Ding +2
Deep reinforcement learning (DRL) has successfully solved various problems recently, typically with a unimodal policy representation. However, grasping distinguishable skills for s…
DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos
Haoqi Yuan, Ruihai Wu, Andrew Zhao +3
Learning an accurate model of the environment is essential for model-based control tasks. Existing methods in robotic visuomotor control usually learn from data with heavily labell…
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding
Yunze Liu, Li Yi, Shanghang Zhang +3
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used a…
End-to-End Object Detection with Adaptive Clustering Transformer
Minghang Zheng, Peng Gao, Renrui Zhang +4
End-to-end Object Detection with Transformer (DETR)proposes to perform object detection with Transformer and achieve comparable performance with two-stage object detection like Fas…
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
Chu-ran Wang, Jing Li, Fandong Zhang +4
Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions…
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
Qingrui Zhang, Hao Dong, Wei Pan
Decentralized multi-agent control has broad applications, ranging from multi-robot cooperation to distributed sensor networks. In decentralized multi-agent control, systems are com…
Efficient Reinforcement Learning Development with RLzoo
Zihan Ding, Tianyang Yu, Yanhua Huang +5
Many researchers and developers are exploring for adopting Deep Reinforcement Learning (DRL) techniques in their applications. They however often find such an adoption challenging.…
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…