activity
20222026
most citedSim-to-Real Strategy for Spatially Aware Robot Navigation in Uneven Outdoor Environments

4 citations · 7 across the 10 of their papers we have counts for

collaborators
Showing cs.ROShow all

12 papers · 1 filter

cs.RO2025

DR. Nav: Semantic-Geometric Representations for Proactive Dead-End Recovery and Navigation

Vignesh Rajagopal, Kasun Weerakoon Kulathun Mudiyanselage, Gershom Devake Seneviratne +5

We present DR. Nav (Dead-End Recovery-aware Navigation), a novel approach to autonomous navigation in scenarios where dead-end detection and recovery are critical, particularly in…

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.RO2025

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.…

cs.RO2025

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…

cs.RO2025

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…

cs.RO2025

AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning

Yangzhe Kong, Daeun Song, Jing Liang +3

We present a novel method, AutoSpatial, an efficient approach with structured spatial grounding to enhance VLMs' spatial reasoning. By combining minimal manual supervision with lar…