arXiv Machine Learning

ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research

arXiv:2506. 22174v3 Announce Type: replace-cross Abstract: The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport.

arXiv Computer Vision
Sep 18

Towards Scaling Marine Perception with Synthetic Data

The paper introduces an extension to the OceanSim underwater perception simulator, adding a Synthetic Data Generation pipeline that produces large, automatically labeled, photorealistic datasets with configurable scene and sensor settings. The authors evaluate this pipeline on a real-world sea urchin detection task, examining how different synthetic scene variations influence sim-to-real performance. They discuss the pipeline’s findings, limitations, and future directions for improving rendering fidelity, scene diversity, and sim-to-real generalization.

By Haoyu Ma, Onur Bagoren, Anja Sheppard, Elias Fandi, Ashrith Edukulla, Tanner Aslan, Natasha Sieh, Jingyu Song, Katherine A. Skinner
arXiv Machine Learning
Aug 31

OceanGym: A Benchmark Environment for Underwater Embodied Agents

OceanGym is the first comprehensive benchmark for underwater embodied agents, featuring eight realistic task domains and a unified agent framework powered by Multi‑modal Large Language Models (MLLMs). It challenges agents to process optical and sonar data, navigate complex environments, and achieve long‑horizon goals amid low visibility and dynamic currents. Experiments show significant performance gaps between current MLLM agents and human experts, underscoring the difficulty of perception, planning, and adaptability in ocean settings.

By Yida Xue, Mingjun Mao, Xiangyuan Ru, Yuqi Zhu, Baochang Ren, Shuofei Qiao, Mengru Wang, Shumin Deng, Xinyu An, Ningyu Zhang, Ying Chen, Huajun Chen
arXiv Machine Learning
Aug 13

IoT-Enabled Autonomous Maritime Navigation in Smart Ports: A Curriculum-Guided Shared Policy Learning Framework

arXiv:2608. 11597v1 Announce Type: cross Abstract: As smart port infrastructures increasingly rely on autonomous maritime devices enabled by the Internet of Things (IoT), ensuring reliable onboard navigation intelligence has become a critical challenge for safe and scalable operations in congested waterways.

By Yuqing Lin, Rangya Zhang, Kum Fai Yuen
arXiv AI
Sep 4

A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle

The paper introduces a low‑cost, open experimental platform for end‑to‑end autonomous driving on miniature Ackermann vehicles, combining a physical car, printed track, data collection tools, trajectory registration, and a Webots digital twin. It implements command‑conditioned behavior cloning, achieving a mean cross‑track error of 6.1 cm on the real vehicle and demonstrating the impact of camera field of view in simulation. Using synthetic data from the digital twin and a sim‑to‑real image translator, a higher‑capacity policy trained on both synthetic and real data completes all four track routes, outperforming the baseline trained only on real data.

By Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer
arXiv AI
Sep 23

RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles

RiverVLN introduces the first benchmark for long‑horizon vision‑language navigation (VLN) of unmanned surface vehicles (USVs) in continuous riverine motion. The PGT‑NAV framework converts navigation instructions into an ordered sequence of visually verifiable semantic phases, maintaining an active phase online through grounded visual and motion evidence. This phase‑grounded approach reduces recursive position and heading drift, achieving a 0.79 success rate in Unity‑ROS closed‑loop tests and demonstrating transfer to real‑world USV deployment.

By Jieling Wu, Yuehao Huang, Jiajun Lv, Tao Huang, Yong Liu, Weiwei Liu
arXiv Machine Learning
4d ago

IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking

arXiv:2606.08729v2 Announce Type: replace-cross Abstract: Developing navigation policies requires simulation scenarios that support repeatable training and evaluation. Despite the availability of num...

By Ruihua Han, Shuai Wang, Chengyang Li, Rui Gao, Xinyi Wang, Zhe Liu, Guoliang Li, Yupu Lu, Qi Hao, Jia Pan, Hengshuang Zhao