Kasun Weerakoon — 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

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 8 Source ↗Scholar ↗

2025

Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation

Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou +2

IROS 2025

Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies due to sample inefficiency of RL. In this work,

Cited by 4 Source ↗Scholar ↗

2025

HALO : Human Preference Aligned Offline Reward Learning for Robot Navigation

Gershom Seneviratne, Jianyu An, Sahire Ellahy, Kasun Weerakoon, Mohamed Bashir Elnoor, Jonathan Deepak Kannan +2

CoRL 2025poster

In this paper, we introduce HALO, a novel Offline Reward Learning algorithm that quantifies human intuition in navigation into a vision-based reward function for robot navigation. HALO learns a reward model from offline data, leveraging expert trajectories collected from mobile robots. During traini…

Cited by 0 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 ↗

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 52 Source ↗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 5 Source ↗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 26 Source ↗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 5 Source ↗Scholar ↗

2023

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

Jing Liang, Kasun Weerakoon, Tianrui Guan, Nare Karapetyan, Dinesh Manocha

RA-L 2023

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 our approach produces policies that result in stable and reliable robot navigation on complex terrains. Based on the Prox

Cited by 20 Source ↗Scholar ↗

2023

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization

Souradip Chakraborty, Amrit Singh Bedi, Kasun Weerakoon, Prithvi Poddar, Alec Koppel, Pratap Tokekar +1

ICRA 2023poster

In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems. Sparse rewards are common in continuous control robotics tasks such as manipulation and navigation and make the learning problem…

Cited by 3 Source ↗Scholar ↗

2023

GrASPE: Graph Based Multimodal Fusion for Robot Navigation in Outdoor Environments

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Jing Liang, Tianrui Guan, Utsav Patel, Dinesh Manocha

RA-L 2023

We present a novel trajectory traversability estimation and planning algorithm for robot navigation in complex outdoor environments. We incorporate multimodal sensory inputs from an RGB camera, 3D LiDAR, and the robot's odometry sensor to train a prediction model to estimate candidate trajectories'

Cited by 78 Source ↗Scholar ↗

2023

VERN: Vegetation-Aware Robot Navigation in Dense Unstructured Outdoor Environments

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Tianrui Guan, Mason Russell, Damon Conover, Jason Pusey +1

IROS 2023poster

We propose a novel method for autonomous legged robot navigation in densely vegetated environments with a variety of pliable/traversable and non-pliable/untraversable vegetation. We present a novel few-shot learning classifier that can be trained on a few hundred RGB images to differentiate flora th…

Cited by 18 Source ↗Scholar ↗

2022

GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha

RA-L 2022

We propose GA-Nav, a novel group-wise attention mechanism to identify safe and navigable regions in unstructured environments from RGB images. Our group-wise attention method extracts multi-scale features from each type of terrain independently and classifies terrains based on their navigability lev

Cited by 161 Source ↗code ↗Scholar ↗

2022

HTRON: Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm

Kasun Weerakoon, Souradip Chakraborty, Nare Karapetyan, Adarsh Jagan Sathyamoorthy, Amrit Bedi, Dinesh Manocha

CoRL 2022poster

We present a novel approach to improve the performance of deep reinforcement learning (DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on carefully designed dense reward functions that learn the efficient behavior in an environment. We circumvent this issue by work…

Cited by 13 Source ↗Scholar ↗

2022

TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel, Dinesh Manocha

ICRA 2022poster

We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attentio…

Cited by 82 Source ↗Scholar ↗

2022

TerraPN: Unstructured Terrain Navigation using Online Self-Supervised Learning

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Tianrui Guan, Jing Liang, Dinesh Manocha

IROS 2022poster

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness, deformability, etc.) of complex outdoor terrains directly from robot-terrain interactions through self-supervised learning, and uses it for autonomous robot navigation. Our method uses RGB images of terrain s…

Cited by 62 Source ↗Scholar ↗