4 citations · 22 across the 23 of their papers we have counts for
24 papers · 1 filter
Motion Planning for Mobile Manipulators Navigating Doorways via Model Predictive Control
Kasra Sinaei, Kasun Weerakoon, Christopher Bradley +2
Navigating doorways is a fundamental capability for mobile manipulators operating in human environments, requiring coordinated motion between the mobile base and manipulator arm. T…
StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance
Hung-Chieh Wu, Xiaopan Zhang, Kasra Sinaei +6
This paper proposes StratMamba, a dual-stream Mamba-based temporal modeling architecture, to more efficiently capture long-horizon temporal dependencies required for robot navigati…
HALO: Human Preference Aligned Offline Reward Learning for Robot Navigation
Gershom Seneviratne, Jianyu An, Sahire Ellahy +5
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.…
MOSU: Autonomous Long-range Robot Navigation with Multi-modal Scene Understanding
Jing Liang, Kasun Weerakoon, Daeun Song +3
We present MOSU, a novel autonomous long-range navigation system that enhances global navigation for mobile robots through multimodal perception and on-road scene understanding. MO…
ViLAM: Distilling Vision-Language Reasoning into Attention Maps for Social Robot Navigation
Mohamed Elnoor, Kasun Weerakoon, Gershom Seneviratne +3
We introduce ViLAM, a novel method for distilling vision-language reasoning from large Vision-Language Models (VLMs) into spatial attention maps for socially compliant robot naviga…
BehAV: Behavioral Rule Guided Autonomy Using VLMs for Robot Navigation in Outdoor Scenes
Kasun Weerakoon, Mohamed Elnoor, Gershom Seneviratne +5
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 interpret…
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…
CROSS-GAiT: Cross-Attention-Based Multimodal Representation Fusion for Parametric Gait Adaptation in Complex Terrains
Gershom Seneviratne, Kasun Weerakoon, Mohamed Elnoor +5
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…
TOPGN: Real-time Transparent Obstacle Detection using Lidar Point Cloud Intensity for Autonomous Robot Navigation
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor +2
We present TOPGN, a novel method for real-time transparent obstacle detection for robot navigation in unknown environments. We use a multi-layer 2D grid map representation obtained…
CoNVOI: Context-aware Navigation using Vision Language Models in Outdoor and Indoor Environments
Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor +6
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: fir…
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…
AdVENTR: Autonomous Robot Navigation in Complex Outdoor Environments
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor +1
We present a novel system, AdVENTR for autonomous robot navigation in unstructured outdoor environments that consist of uneven and vegetated terrains. Our approach is general and c…
Using Lidar Intensity for Robot Navigation
Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor +1
We present Multi-Layer Intensity Map, a novel 3D object representation for robot perception and autonomous navigation. Intensity maps consist of multiple stacked layers of 2D grid…
VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor +1
We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our metho…
ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments
Mohamed Elnoor, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon +1
We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our appro…
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation
Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle +5
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 sp…
RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback
Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar +6
Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may resul…
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…
HTRON:Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm
Kasun Weerakoon, Souradip Chakraborty, Nare Karapetyan +3
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 carefull…
Sim-to-Real Strategy for Spatially Aware Robot Navigation in Uneven Outdoor Environments
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Dinesh Manocha
Deep Reinforcement Learning (DRL) is hugely successful due to the availability of realistic simulated environments. However, performance degradation during simulation to real-world…
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…
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…
TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel +1
We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a novel fully-trained Deep Reinforcement Learning (DRL) network that uses e…
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…