68 citations · 154 across the 28 of their papers we have counts for
28 papers
HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation
An Liu, Bingxi Liu, Hongyu Ding +6
Vision-and-language navigation (VLN) enables robots to follow instructions in previously unseen environments. Recently, a training-free paradigm has emerged: the robot queries a mu…
TempoFit: Plug-and-Play Layer-Wise Temporal KV Memory for Long-Horizon Vision-Language-Action Manipulation
Jun Sun, Boyu Yang, Jiahao Zhang +7
Pretrained Vision-Language-Action (VLA) policies have achieved strong single-step manipulation, but their inference remains largely memoryless, which is brittle in non-Markovian lo…
Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
Yiou Huang, Ning Ma, Weichu Zhao +4
Imitation learning (IL) has shown strong potential for contact-rich precision insertion tasks. However, its practical deployment is often hindered by covariate shift and the need f…
RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning
Jinrui Liu, Bingyan Nie, Boyu Li +4
Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully. Despite the succe…
Robotic Sim-to-Real Transfer for Long-Horizon Pick-and-Place Tasks in the Robotic Sim2Real Competition
Ming Yang, Hongyu Cao, Lixuan Zhao +2
This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex long-horizon tasks involving navigation, recognition, grasping, and stacking in…
Sample-efficient Unsupervised Policy Cloning from Ensemble Self-supervised Labeled Videos
Xin Liu, Yaran Chen, Haoran Li
Current advanced policy learning methodologies have demonstrated the ability to develop expert-level strategies when provided enough information. However, their requirements, inclu…
GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation
Wenbo Cui, Chengyang Zhao, Songlin Wei +5
Effectively manipulating articulated objects in household scenarios is a crucial step toward achieving general embodied artificial intelligence. Mainstream research in 3D vision ha…
PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
Yupeng Zheng, Zebin Xing, Qichao Zhang +8
Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios bu…
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning
Xin Liu, Yaran Chen, Dongbin Zhao
In visual Reinforcement Learning (RL), upstream representation learning largely determines the effect of downstream policy learning. Employing auxiliary tasks allows the agent to e…
Advancing Object Goal Navigation Through LLM-enhanced Object Affinities Transfer
Mengying Lin, Shugao Liu, Dingxi Zhang +4
Object-goal navigation requires mobile robots to efficiently locate targets with visual and spatial information, yet existing methods struggle with generalization in unseen environ…
RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks
Yaran Chen, Wenbo Cui, Yuanwen Chen +4
Robotic agents must master common sense and long-term sequential decisions to solve daily tasks through natural language instruction. The developments in Large Language Models (LLM…
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
Xin Liu, Yaran Chen, Dongbin Zhao
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Unsupervised s…
Multi-modal Learning based Prediction for Disease
Yaran Chen, Xueyu Chen, Yu Han +4
Non alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, which can be predicted accurately to prevent advanced fibrosis and cirrhosis. While, a…
Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
Xin Liu, Yaran Chen, Haoran Li +2
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Unsupervised c…
NeuronsGym: A Hybrid Framework and Benchmark for Robot Tasks with Sim2Real Policy Learning
Haoran Li, Shasha Liu, Mingjun Ma +3
The rise of embodied AI has greatly improved the possibility of general mobile agent systems. At present, many evaluation platforms with rich scenes, high visual fidelity and vario…
BViT: Broad Attention based Vision Transformer
Nannan Li, Yaran Chen, Weifan Li +2
Recent works have demonstrated that transformer can achieve promising performance in computer vision, by exploiting the relationship among image patches with self-attention. While…
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search
Zixiang Ding, Yaran Chen, Nannan Li +2
Different from other deep scalable architecture-based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad scalable architecture which consists of convolution a…
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search
Jiaqi Li, Haoran Li, Yaran Chen +5
Currently, an increasing number of model pruning methods are proposed to resolve the contradictions between the computer powers required by the deep learning models and the resourc…
MVM3Det: A Novel Method for Multi-view Monocular 3D Detection
Li Haoran, Duan Zicheng, Ma Mingjun +3
Monocular 3D object detection encounters occlusion problems in many application scenarios, such as traffic monitoring, pedestrian monitoring, etc., which leads to serious false neg…
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Nannan Li, Yu Pan, Yaran Chen +3
Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performan…
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search
Zixiang Ding, Yaran Chen, Nannan Li +1
In this paper, we propose BNAS-v2 to further improve the efficiency of NAS, embodying both superiorities of BCNN simultaneously. To mitigate the unfair training issue of BNAS, we e…
ContourRend: A Segmentation Method for Improving Contours by Rendering
Junwen Chen, Yi Lu, Yaran Chen +2
A good object segmentation should contain clear contours and complete regions. However, mask-based segmentation can not handle contour features well on a coarse prediction grid, th…
BiFNet: Bidirectional Fusion Network for Road Segmentation
Haoran Li, Yaran Chen, Qichao Zhang +1
Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is ma…
ModuleNet: Knowledge-inherited Neural Architecture Search
Yaran Chen, Ruiyuan Gao, Fenggang Liu +1
Although Neural Architecture Search (NAS) can bring improvement to deep models, they always neglect precious knowledge of existing models. The computation and time costing property…