5 citations · 26 across the 40 of their papers we have counts for
9 papers · 1 filter
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…
MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models
Xiyang Wu, Zongxia Li, Jihui Jin +7
Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial interactions. We present a nove…
Bi-VLM: Pushing Ultra-Low Precision Post-Training Quantization Boundaries in Vision-Language Models
Xijun Wang, Junyun Huang, Rayyan Abdalla +3
We address the critical gap between the computational demands of vision-language models and the possible ultra-low-bit weight precision (bitwidth bits) we can use for highe…
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…
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…
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Zongxia Li, Xiyang Wu, Guangyao Shi +6
Vision-Language Models (VLMs) have achieved strong results in video understanding, yet a key question remains: do they truly comprehend visual content or only learn shallow correla…
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…
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…