cs.RO · 2026-08-05 · No. 75

Robotics, 2026-08-05.

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

01 — The papers

7 entries
  1. 01

    LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation

    Fan Yang, Yuting Su, Xiaobo Wang, Yuncheng You, Fugui Fan, Yuting Wu, Minghui Wu, Chenxu Zhao, JiaHong Ning, Peiguang Jing

    cs.RO · cs.AI

    World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve. However, existing WAMs often incur substantial computational overhead. Pixel-space methods often allocate substantial capacity to visual details that may not be directly relevant to control, while some latent-space methods require multi-stage training to construct...

    arxiv.org/abs/2608.03701 · PDF

  2. 02

    Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance

    Xiucong Zhao, Jindong Tian, Hao Miao

    cs.RO · cs.AI

    Forecasting precise future motion of surrounding agents is essential for reliable autonomous vehicles. However, as the demand for longer prediction horizons increases, existing endpoint-completion or iterative-refine methods increasingly struggle with weak guidance and compounding errors. To tackle the long-horizon prediction challenge, we propose Pivot-Centric Trajectory Prediction (PCTP). By introducing ``pivots'' and focusing on predicting...

    arxiv.org/abs/2608.03521 · PDF

  3. 03

    Principles of Robot Autonomy

    Daniele Gammelli, Joseph Lorenzetti, Katie Luo, Gioele Zardini, Marco Pavone

    cs.RO · cs.AI · cs.CV · eess.SY

    Autonomous robots are moving rapidly from research labs into everyday life - on roads, in the air, in warehouses, and in space. Robot autonomy is no longer solely an academic pursuit, but a collection of mature, field-tested methods and tools that practitioners rely on in real-world deployments. This book offers a clear, unified introduction to the methods that make this possible. Built on decades of teaching at Stanford, the text develops...

    arxiv.org/abs/2608.03496 · PDF

  4. 04

    Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution

    Weichen Xu, Zhenhua Liu, Lin Luo, Yaobo Liang, Chengtang Yao, Qingyu Mei, Jian Cao, Xixin Cao, Xing Zhang, Jiaolong...

    cs.RO · cs.AI · cs.CV · cs.LG

    Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.e., execution horizon) before replanning, turning replanning into a task-agnostic periodic schedule that is independent of task progress. As a result, when no replanning boundary falls before a critical manipulation stage, it is executed from a stale chunk rather than a freshly replanned one. To address this limitation, we propose...

    arxiv.org/abs/2608.03483 · PDF

  5. 05

    A Low-Cost Hybrid Reservoir Computing Model for Isolated Sign Language Video Recognition

    Nitin Kumar Singh, Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

    cs.RO · cs.AI · cs.CV

    Sign language recognition (SLR) enhances communication between hearing and hearing-impaired individuals. Although deep learning (DL) has achieved promising performance in SLR, its high computational cost limits deployment on edge devices. To address this challenge, we propose a lightweight reservoir computing (RC)-based approach for SLR. In the proposed method, MediaPipe extracts body and hand keypoints to capture the spatial and temporal...

    arxiv.org/abs/2608.03444 · PDF

  6. 06

    Flying over The Uncertain Nature (FORTUNE): Intelligent and Humanistic 3D Path Planning for Low-Altitude Collaboration

    Minghui Liwang, Wenhan Jia, Xinlei Yi, Wenbo Zhu, Yuhan Su, Xianbin Wang

    cs.RO · cs.DC

    The proliferation of low-altitude intelligent agents is increasing the demand for timely and socially responsible collaborative sensing in dynamic urban environments. However, jointly addressing heterogeneous spatiotemporal demands, environmental uncertainty, and human-centered operational constraints remains challenging. This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands. Unlike existing work...

    arxiv.org/abs/2608.03408 · PDF

  7. 07

    Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning

    Ghadeer Elmkaiel, Michael Muehlebach

    cs.RO · cs.AI · eess.SY

    The development and testing of advanced aerial robots require experiments in controlled environments with tailored airflow profiles. This paper presents an online learning algorithm for controlling the complex airflow field in a multi-fan vertical wind tunnel. Our method combines a simplified physical model with iterative, measurement-based learning, enabling sample-efficient convergence to desired airflow distributions. We demonstrate the...

    arxiv.org/abs/2608.03378 · PDF

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