activity
20192026
most citedAZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning

20 citations · 45 across the 25 of their papers we have counts for

collaborators

26 papers

cs.RO2026

EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos

Yifan Zhong, Zhang Chen, Tianrui Guan +15

The enduring vision of general-purpose robots serving humanity hinges fundamentally on policy steerability. However, prevailing paradigms of learning from expert demonstrations dem…

cs.RO2026

Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement

Montana Hoover, Jing Liang, Tianrui Guan +1

We introduce Paired-CSLiDAR (CSLiDAR), a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement: refining a ground-scan pose within a 50 m-radius aerial crop. T…

cs.RO2025

NavMoE: Hybrid Model- and Learning-based Traversability Estimation for Local Navigation via Mixture of Experts

Botao He, Amir Hossein Shahidzadeh, Yu Chen +8

This paper explores traversability estimation for robot navigation. A key bottleneck in traversability estimation lies in efficiently achieving reliable and robust predictions whil…

cs.RO2024★ 2 cited

Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments

Mohamed Elnoor, Kasun Weerakoon, Gershom Seneviratne +5

We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, u…

cs.CV2024

FALCON: Future-Aware Learning with Contextual Object-Centric Pretraining for UAV Action Recognition

Ruiqi Xian, Xiyang Wu, Tianrui Guan +3

We introduce FALCON, a unified self-supervised video pretraining approach for UAV action recognition from raw RGB aerial footage, requiring no additional preprocessing at inference…

cs.CV2024★ 2 cited

LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation

Tianrui Guan, Yurou Yang, Harry Cheng +5

In this paper, we present LOC-ZSON, a novel Language-driven Object-Centric image representation for object navigation task within complex scenes. We propose an object-centric image…

cs.CV2024

AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales

Tianrui Guan, Ruiqi Xian, Xijun Wang +4

We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps. AGL-NET tackles two critical challenges: bridging the represe…

cs.RO2024

AMCO: Adaptive Multimodal Coupling of Vision and Proprioception for Quadruped Robot Navigation in Outdoor Environments

Mohamed Elnoor, Kasun Weerakoon, Adarsh Jagan Sathyamoorthy +3

We present AMCO, a novel navigation method for quadruped robots that adaptively combines vision-based and proprioception-based perception capabilities. Our approach uses three cost…

cs.RO2024★ 2 cited

On the Vulnerability of LLM/VLM-Controlled Robotics

Xiyang Wu, Souradip Chakraborty, Ruiqi Xian +6

In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities. While…

cs.CV2023★ 5 cited

HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

Tianrui Guan, Fuxiao Liu, Xiyang Wu +9

We introduce HallusionBench, a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visu…

cs.MA2023★ 1 cited

iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning

Xiyang Wu, Rohan Chandra, Tianrui Guan +2

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intenti…

cs.CV2023

SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition

Xijun Wang, Ruiqi Xian, Tianrui Guan +2

We present a new learning approach, Soft Conditional Prompt Learning (SCP), which leverages the strengths of prompt learning for aerial video action recognition. Our approach is de…

cs.CV2023★ 20 cited

AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning

Xijun Wang, Ruiqi Xian, Tianrui Guan +4

We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-…

cs.CV2023

CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition

Tianrui Guan, Aswath Muthuselvam, Montana Hoover +5

We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting. Cross-source point cloud data corresponds to…

cs.RO2022

VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain

Tianrui Guan, Ruitao Song, Zhixian Ye +1

We present a visual and inertial-based terrain classification network (VINet) for robotic navigation over different traversable surfaces. We use a novel navigation-based labeling s…

cs.RO2022★ 2 cited

GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Jing Liang +3

We present a novel trajectory traversability estimation and planning algorithm for robot navigation in complex outdoor environments. We incorporate multimodal sensory inputs from a…

cs.RO2022★ 1 cited

AdaptiveON: Adaptive Outdoor Local Navigation Method For Stable and Reliable Actions

Jing Liang, Kasun Weerakoon, Tianrui Guan +2

We present a novel outdoor navigation algorithm to generate stable and efficient actions to navigate a robot to reach a goal. We use a multi-stage training pipeline and show that o…

cs.CV2022

FAR: Fourier Aerial Video Recognition

Divya Kothandaraman, Tianrui Guan, Xijun Wang +3

We present an algorithm, Fourier Activity Recognition (FAR), for UAV video activity recognition. Our formulation uses a novel Fourier object disentanglement method to innately sepa…

cs.RO2022

TerraPN: Unstructured Terrain Navigation using Online Self-Supervised Learning

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Tianrui Guan +2

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness, deformability, etc.) of complex outdoor terrains directly from robot-terrain interaction…

cs.RO2021★ 1 cited

TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators

Tianrui Guan, Zhenpeng He, Ruitao Song +2

We present a terrain traversability mapping and navigation system (TNS) for autonomous excavator applications in an unstructured environment. We use an efficient approach to extrac…

cs.CV2021★ 6 cited

M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

Tianrui Guan, Jun Wang, Shiyi Lan +4

We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales bas…

cs.RO2021★ 1 cited

GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

Tianrui Guan, Divya Kothandaraman, Rohan Chandra +3

We propose GANav, a novel group-wise attention mechanism to identify safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach cla…

cs.RO2020★ 1 cited

OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors

Jing Liang, Yi-Ling Qiao, Tianrui Guan +1

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our s…

cs.RO2020

Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds

Adarsh Jagan Sathyamoorthy, Utsav Patel, Tianrui Guan +1

We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that arises when a robot navigates through dense scenarios and crowds. Our method senses and exp…