most citedCan Large Vision Language Models Read Maps Like a Human?

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2026

UNCLE-Grasp: A Task-Adapted Framework for Uncertainty-Aware Grasping of Leaf-Occluded Strawberries

Malak Mansour, Ali Abouzeid, Zezhou Sun +3

Robotic strawberry harvesting remains challenging under partial occlusion, where leaves obscure fruit geometry and make grasp decisions based on a single shape estimate unreliable.…

cs.RO2025

Imagination at Inference: Synthesizing In-Hand Views for Robust Visuomotor Policy Inference

Haoran Ding, Anqing Duan, Zezhou Sun +2

Visual observations from different viewpoints can significantly influence the performance of visuomotor policies in robotic manipulation. Among these, egocentric (in-hand) views of…

cs.RO2025

A Hybrid Hinge-Beam Continuum Robot with Passive Safety Capping for Real-Time Fatigue Awareness

Tongshun Chen, Zezhou Sun, Yanhan Sun +3

Cable-driven continuum robots offer high flexibility and lightweight design, making them well-suited for tasks in constrained and unstructured environments. However, prolonged use…

cs.RO2025

GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Model

Ali Abouzeid, Malak Mansour, Qinbo Sun +2

Vision-Language-Action (VLA) models often fail to generalize to unseen camera viewpoints, a limitation stemming from their difficulty in inferring robust 3D geometry from 2D images…

cs.RO2025

Towards Safe Imitation Learning via Potential Field-Guided Flow Matching

Haoran Ding, Anqing Duan, Zezhou Sun +4

Deep generative models, particularly diffusion and flow matching models, have recently shown remarkable potential in learning complex policies through imitation learning. However,…

cs.CV2025★ 1 cited

Can Large Vision Language Models Read Maps Like a Human?

Shuo Xing, Zezhou Sun, Shuangyu Xie +6

In this paper, we introduce MapBench-the first dataset specifically designed for human-readable, pixel-based map-based outdoor navigation, curated from complex path finding scenari…