activity
20182026
most citedSM-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving

36 citations · 60 across the 14 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.RO2026

DPed-VLN: A Benchmark for Socially Compliant Vision-and-Language Navigation in Dynamic Pedestrian Environments

Haojie Dai, Xiangyi Wang, Liuyi Wang +5

Vision-and-language navigation (VLN) has advanced rapidly in static indoor environments, but robots operating in human-populated spaces must ground language while responding to mov…

cs.RO2026

A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation

Liuyi Wang, Kai Sheng, Zongtao He +8

Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness…

cs.RO2026

KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility

Zhiming Xu, Haodong Yang, Chengju Liu +2

Deep Reinforcement Learning (DRL) has shown promise for social navigation, yet its real-world deployment remains hindered by a persistent sim-to-real gap arising from simplified fi…

cs.RO2026

Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation

Zhiming Xu, Weitao Zhou, Xianghui Pan +4

Real-world dynamics shifts pose a critical challenge for reinforcement learning in robotics, as policies tightly coupled to nominal environments often fail catastrophically when ph…

cs.CV2026

P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation

Kai Sheng, Liuyi Wang, Haojie Dai +5

Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen environments. Existing zero-sho…

cs.LG2026

Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces

Jiayuan Du, Yuebing Song, Yiming Zhao +6

End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, a…