118 citations · 143 across the 21 of their papers we have counts for
21 papers
Exploring Spatial Representation to Enhance LLM Reasoning in Aerial Vision-Language Navigation
Yunpeng Gao, Zhigang Wang, Pengfei Han +3
Aerial Vision-and-Language Navigation (VLN) is a novel task enabling Unmanned Aerial Vehicles (UAVs) to navigate in outdoor environments through natural language instructions and v…
COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
Kehui Liu, Zixin Tang, Dong Wang +3
Leveraging the powerful reasoning capabilities of large language models (LLMs), recent LLM-based robot task planning methods yield promising results. However, they mainly focus on…
AlignBot: Aligning VLM-powered Customized Task Planning with User Reminders Through Fine-Tuning for Household Robots
Zhaxizhuoma Zhaxizhuoma, Pengan Chen, Ziniu Wu +7
This paper presents AlignBot, a novel framework designed to optimize VLM-powered customized task planning for household robots by effectively aligning with user reminders. In domes…
Depth Helps: Improving Pre-trained RGB-based Policy with Depth Information Injection
Xincheng Pang, Wenke Xia, Zhigang Wang +4
3D perception ability is crucial for generalizable robotic manipulation. While recent foundation models have made significant strides in perception and decision-making with RGB-bas…
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance
Jingxian Lu, Wenke Xia, Dong Wang +4
Online Imitation Learning struggles with the gap between extensive online exploration space and limited expert trajectories, hindering efficient exploration due to inaccurate rewar…
LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Rendering and Control
Delin Qu, Qizhi Chen, Pingrui Zhang +5
This paper scales object-level reconstruction to complex scenes, advancing interactive scene reconstruction. We introduce two datasets, OmniSim and InterReal, featuring 28 scenes w…
Learning Manipulation by Predicting Interaction
Jia Zeng, Qingwen Bu, Bangjun Wang +12
Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
Yiwen Tang, Ray Zhang, Jiaming Liu +8
Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts…
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
Linglin Jing, Yiming Ding, Yunpeng Gao +7
Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions, which cannot be addres…
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge Transfer
Linglin Jing, Ying Xue, Xu Yan +7
The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences. However, it remains a challenging task due to the spars…
Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Jiawei Sun, Bin Zhao, Dong Wang +5
Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness. However, the comput…
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
Chi Yan, Delin Qu, Dan Xu +4
In this paper, we introduce \textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better bala…
Implicit Event-RGBD Neural SLAM
Delin Qu, Chi Yan, Dong Wang +4
Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting v…
Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
Wenke Xia, Dong Wang, Xincheng Pang +4
Generalizable articulated object manipulation is essential for home-assistant robots. Recent efforts focus on imitation learning from demonstrations or reinforcement learning in si…
Robust Quadrupedal Locomotion via Risk-Averse Policy Learning
Jiyuan Shi, Chenjia Bai, Haoran He +6
The robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged locomotio…
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Weichuang Li, Longhao Zhang, Dong Wang +7
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely…
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
Xiangyang Zhu, Renrui Zhang, Bowei He +4
The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks. To improve its capacity on downstream tasks, few-…
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
Yihao Wang, Zhigang Wang, Bin Zhao +3
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely…
Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction
Delin Qu, Yizhen Lao, Zhigang Wang +3
This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer from two main drawbacks. Firs…
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
Zoey Guo, Yiwen Tang, Ray Zhang +4
Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view c…
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Renrui Zhang, Ziyu Guo, Rongyao Fang +5
Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to e…