cs.RO · 2026-08-13 · No. 83

Robotics, 2026-08-13.

5 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

    Jean-Pierre Busch, Guido Linden, Jan Bergmann, Lutz Eckstein

    cs.RO · cs.AI · cs.LG

    Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of...

    arxiv.org/abs/2608.12198 · PDF

  2. 02

    Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL

    Martin Schuck, Maks Sorokin, Simone Manni, Duy Ta, Angela P. Schoellig, Marco Hutter, Simon Le Cleac'H, Jan Brüdigam

    cs.RO · cs.AI

    Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping. To bypass this limitation, we leverage Sample-based Model Predictive Control (SMPC) entirely in simulation as an automated, rapidly tunable expert to generate massive offline datasets. Because this data solves the fundamental...

    arxiv.org/abs/2608.12063 · PDF

  3. 03

    G0.5: One Autoregressive Stream for Robot Reasoning and Action

    Yicheng Liu, Zibin Dong, Baijun Ye, Tianyuan Yuan, Tao Jiang, Anqi Yang, Shicheng Cao, Haonan Liu, Yue Sun, Zihan...

    cs.RO · cs.AI

    The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert. This makes the VLM a context encoder rather than a decision-maker. We introduce G0.5, a pretrained autoregressive VLA in which a single transformer decoder emits reasoning and action tokens under a single objective. Three components make this tractable at foundation-model scale: a learnable...

    arxiv.org/abs/2608.11739 · PDF

  4. 04

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

    Yuqing Lin, Rangya Zhang, Kum Fai Yuen

    cs.RO · cs.LG

    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. This paper investigates onboard autonomous navigation for such IoT devices under partial observability and dense traffic conditions. A curriculum-guided reinforcement learning framework...

    arxiv.org/abs/2608.11597 · PDF

  5. 05

    RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation

    Yueyuan Li, Zexi Chen, Weijie Xi, Mingyang Jiang, Songan Zhang, Hanyang Zhuang, Ming Yang

    cs.RO · cs.AI

    Autonomous driving simulation requires diverse and scalable lane-level HD maps to support long-horizon evaluation across complex road networks. Existing approaches either rely on handcrafted or reconstructed real-world maps, which limits scalability, or generate only local road structures rather than complete HD maps. We present RoadWeaver, a coarse-to-fine framework for from-scratch generation of diverse, large-scale HD maps. RoadWeaver...

    arxiv.org/abs/2608.11580 · PDF

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