Vignesh Rajagopal — accepted AI-conference 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 22 Source ↗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 7 Source ↗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 4 Source ↗Scholar ↗