cs.RO · 2026-07-08 · No. 47

Robotics, 2026-07-08.

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

    Learning to Throw Objects Safely in Multi-Obstacle Environments

    Mohammadreza Kasaei, Klemen Voncina, Hamidreza Kasaei

    cs.RO · cs.CV · cs.LG

    Robotic throwing enables fast and efficient object placement beyond the robot's immediate workspace, but reliable throwing in cluttered environments remains underexplored. Existing approaches, such as TossingBot, learn throwing strategies from visual input but assume obstacle-free settings. In this paper, we address the problem of throwing objects into a target basket while avoiding obstacles placed randomly in the scene. We introduce a...

    arxiv.org/abs/2607.06388 · PDF

  2. 02

    Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement

    Ryuji Oi, Hikari Otsuka, Kosuke Matsushima, Yuki Ichikawa, Masato Motomura, Tatsuya Kaneko, Daichi Fujiki

    cs.RO · cs.CV · cs.LG

    Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow matching-based VLA models have shown remarkable success due to their capability to generate precise and smooth action sequences and capture multimodal distributions. However, the iterative denoising process in the action head acts as a major computational bottleneck, posing a critical challenge for real-time...

    arxiv.org/abs/2607.06370 · PDF

  3. 03

    Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction

    Antonio Andriella, Jauwairia Nasir, Andrea Rezzani, Alyssa Kubota, Dimitri Lacroix, Tamlin Love, Aniol Civit, Vicky...

    cs.RO · cs.AI · cs.HC

    While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interaction contexts. This gap is particularly critical in HRI, where robots' embodiment and social presence can amplify and reshape such risks or generate new types of risks....

    arxiv.org/abs/2607.06344 · PDF

  4. 04

    FORGE: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning

    Chuhao Zhou, Liquan Wang, Shuxin Cao, Xiangyu Chen, Yuxuan Hu, Boyu Ma, Animesh Garg, Jianfei Yang

    cs.RO · cs.AI · cs.CV

    While humans readily repurpose a book, a stone, or a shoe to drive a nail, robots trained on specific tools fail to transfer the same function to novel ones -- a gap we formalize as functional generalization. Such tools share a common functional intent that is visually recognizable, yet this perceptual similarity does not carry over to action space, where each tool demands an entirely different motor pattern. To bridge this gap, we explore...

    arxiv.org/abs/2607.05780 · PDF

  5. 05

    IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction

    Honglin Wang, Shiyao Pan, Yun-Fu Liu

    cs.RO · cs.AI · cs.CV · cs.LG

    Multi-agent motion prediction is essential for automated vehicles to understand the intentions of surrounding vehicles. However, previous prediction-based and anchor-based methods have limitations in mode diversity and prediction accuracy, respectively. These limitations may cause inadequate safety assessments and behavioral deviations in automated vehicles. To address this issue, a mode-world weighted regression loss is proposed to bridge...

    arxiv.org/abs/2607.05705 · PDF

  6. 06

    Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba

    Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Wolfgang Utschick, Michael Botsch

    cs.RO · cs.LG

    Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction. Existing approaches provide drift correction through dedicated infrastructure, expensive external sensors, or complex multi-sensor fusion, each introducing practical deployment barriers. We propose Evidential Velocity Correction using Mamba...

    arxiv.org/abs/2607.05669 · PDF

  7. 07

    Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors

    Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Wolfgang Utschick, Michael Botsch

    cs.RO · cs.AI · cs.LG

    Accurate and robust localization is essential for autonomous mobility systems in real-world environments. While fusing Inertial Measurement Unit (IMU) data with satellite-based correction signals provides precise vehicle pose estimates, performance degrades substantially during outages. Recent studies indicate that Machine Learning (ML) can improve IMU-based proprioceptive localization, highlighting untapped potential for onboard sensors...

    arxiv.org/abs/2607.05663 · PDF

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