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Mohamed Elnoor

10 accepted papers

2025

Behav: Behavioral Rule Guided Autonomy Using VLMs for Robot Navigation in Outdoor Scenes

Kasun Weerakoon, Mohamed Elnoor, Gershom Seneviratne, Vignesh Rajagopal, Senthil Hariharan Arul, Jing Liang +2
ICRA 2025

We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM), and categorizes the instructions into navigation and behavioral gui

Cited by 22Source ↗Scholar ↗
2025

CROSS-GAiT: Cross-Attention-Based Multimodal Representation Fusion for Parametric Gait Adaptation in Complex Terrains

Gershom Seneviratne, Kasun Weerakoon, Mohamed Elnoor, Vignesh Rajgopal, Harshavarthan Varatharajan, Mohamed Khalid M. Jaffar +2
IROS 2025

We present CROSS-GAiT, a novel algorithm for quadruped robots that uses Cross Attention to fuse terrain representations derived from visual and time-series inputs; including linear accelerations, angular velocities, and joint efforts. These fused representations are used to continuously adjust two c

Cited by 8Source ↗Scholar ↗
2025

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

Mohamed Elnoor, Kasun Weerakoon, Gershom Seneviratne, Ruiqi Xian, Tianrui Guan, Mohamed Khalid M. Jaffar +2
ICRA 2025

We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical grounding that is used to assess intrinsic terrain p

Cited by 15Source ↗Scholar ↗
2024

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

Tianrui Guan, Ruiqi Xian, Xijun Wang, Xiyang Wu, Mohamed Elnoor, Daeun Song +1
IROS 2024poster

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 representation gap between image and points modalities for robust feature matching, and handling inherent scale discrepancies b…

Cited by 1Source ↗code ↗Scholar ↗
2024

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

Mohamed Elnoor, Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Tianrui Guan, Vignesh Rajagopal, Dinesh Manocha
IROS 2024poster

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 maps: general knowledge map; traversability history map; and current proprioception map; which are derived from a robot…

Cited by 4Source ↗Scholar ↗
2024

CoNVOI: Context-aware Navigation using Vision Language Models in Outdoor and Indoor Environments

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor, Anuj Zore, Brian Ichter, Fei Xia +3
IROS 2024

We present CoNVOI, a novel method for autonomous robot navigation in real-world indoor and outdoor environments using Vision Language Models (VLMs). We employ VLMs in two ways: first, we leverage their zero-shot image classification capability to identify the context or scenario (e.g., indoor corrid

Cited by 52Source ↗Scholar ↗
2024

MIM: Indoor and Outdoor Navigation in Complex Environments Using Multi-Layer Intensity Maps

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor, Mason Russell, Jason Pusey, Dinesh Manocha
ICRA 2024poster

We present MIM (Multi-Layer Intensity Map), a novel 3D object representation for robot perception and autonomous navigation. MIMs consist of multiple stacked layers of 2D grid maps each derived from reflected point cloud intensities corresponding to a certain height interval. The different layers of…

Cited by 5Source ↗Scholar ↗
2024

MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation

Jing Liang, Peng Gao, Xuesu Xiao, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Ming C. Lin +1
ICRA 2024poster

We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global planning using limited onboard robot perception in ma…

Cited by 10Source ↗Scholar ↗
2024

ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments

Mohamed Elnoor, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha
RA-L 2024

We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our approach uses sensor data from a legged robot's joint encoders, force, and current sensors to measure the joint positions, fo

Cited by 26Source ↗Scholar ↗
2024

VAPOR: Legged Robot Navigation in Unstructured Outdoor Environments using Offline Reinforcement Learning

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Dinesh Manocha
ICRA 2024poster

We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. Our po…

Cited by 5Source ↗Scholar ↗