13 citations · 44 across the 25 of their papers we have counts for
26 papers
GAINS: Leveraging Inconsistent Human Intervention Signals in Reinforcement Learning
Xinyi Zhang, Yinuo Zhao, Pei Ren +7
Correcting robot manipulation policies through human intervention holds great promise for real-world deployment, yet human operators are inherently imperfect in both the actions th…
Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning
Wenke Xia, Pei Ren, Wenbo Yu +10
Offline-to-online reinforcement learning is promising for generalizable robotic manipulation, yet its full-stack complexity obscures reproduction and diagnosis. Within such systems…
Labimus: A Simulation and Benchmark for Humanoid Dexterous Manipulation in Chemical Laboratory
Yuhan Wu, Zhao Jin, Tao Li +9
Laboratory automation has made remarkable progress through robotic platforms and AI-driven scientific reasoning. However, many laboratory operations (e.g., solid--solid transfer) r…
Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks
Lequn Fu, Yijun Zhong, Xiao Li +4
Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving s…
Real-world Reinforcement Learning from Suboptimal Interventions
Yinuo Zhao, Huiqian Jin, Lechun Jiang +9
Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn fro…
RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
Chengkai Hou, Kun Wu, Jiaming Liu +30
While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstration…
Training-free Generation of Temporally Consistent Rewards from VLMs
Yinuo Zhao, Jiale Yuan, Zhiyuan Xu +6
Recent advances in vision-language models (VLMs) have significantly improved performance in embodied tasks such as goal decomposition and visual comprehension. However, providing a…
SwitchVLA: Execution-Aware Task Switching for Vision-Language-Action Models
Meng Li, Zhen Zhao, Zhengping Che +7
Robots deployed in dynamic environments must be able to not only follow diverse language instructions but flexibly adapt when user intent changes mid-execution. While recent Vision…
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning
Zhiyuan Xu, Yinuo Zhao, Kun Wu +5
Teleoperation is essential for autonomous robot learning, especially in manipulation tasks that require human demonstrations or corrections. However, most existing systems only off…
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
Kun Wu, Yinuo Zhao, Zhiyuan Xu +5
Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a saf…
A Survey on Robotics with Foundation Models: toward Embodied AI
Zhiyuan Xu, Kun Wu, Junjie Wen +4
While the exploration for embodied AI has spanned multiple decades, it remains a persistent challenge to endow agents with human-level intelligence, including perception, learning,…
Object-Centric Instruction Augmentation for Robotic Manipulation
Junjie Wen, Yichen Zhu, Minjie Zhu +8
Humans interpret scenes by recognizing both the identities and positions of objects in their observations. For a robot to perform tasks such as \enquote{pick and place}, understand…
SM: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects
Haowen Wang, Zhen Zhao, Zhao Jin +6
Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused o…
Visual Robotic Manipulation with Depth-Aware Pretraining
Wanying Wang, Jinming Li, Yichen Zhu +7
Recent work on visual representation learning has shown to be efficient for robotic manipulation tasks. However, most existing works pretrained the visual backbone solely on 2D ima…
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation
Yinuo Zhao, Kun Wu, Tianjiao Yi +5
Improving generalization is one key challenge in embodied AI, where obtaining large-scale datasets across diverse scenarios is costly. Traditional weak augmentations, such as cropp…
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
Kun Wu, Ning Liu, Zhen Zhao +5
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collect…
Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering
Chengxiang Yin, Zhengping Che, Kun Wu +2
Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature. However, most existing VQA methods are in…
Cross-Modal Reasoning with Event Correlation for Video Question Answering
Chengxiang Yin, Zhengping Che, Kun Wu +3
Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the…
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
Wanying Wang, Yichen Zhu, Yirui Zhou +5
Generative Adversarial Imitation Learning (GAIL) stands as a cornerstone approach in imitation learning. This paper investigates the gradient explosion in two types of GAIL: GAIL w…
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
Haowen Wang, Zhipeng Fan, Zhen Zhao +7
Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric feature…
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Haowen Wang, Zhengping Che, Yufan Yang +6
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent…
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
Mingze Wei, Yaomin Huang, Zhiyuan Xu +7
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly red…
CP: Channel Pruning Plug-in for Point-based Networks
Yaomin Huang, Ning Liu, Zhengping Che +7
Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success ha…
Label-Guided Auxiliary Training Improves 3D Object Detector
Yaomin Huang, Xinmei Liu, Yichen Zhu +7
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently. In this paper, we propose a Label-Guided auxiliary trai…