cs.RO · 2026-10-05 · No. 134

Robotics, 2026-10-05.

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

    EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras

    Kush Hari, Justin Kerr, Nidhya Shivakumar, Samarth Mahapatra, Carmelo Sferrazza, Jiahui Lei, Jitendra Malik, C....

    cs.RO · cs.AI

    Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends to a 3D fixation point in the scene by swiveling two eye viewpoints to center their gaze on it. The resulting images are processed foveally by allocating more visual tokens to the image centers, focusing computation on task-relevant features. Such Active Visual...

    arxiv.org/abs/2610.03710 · PDF

  2. 02

    Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access

    Harry Robertshaw, Weijie Qi, Nikola Fischer, Alejandro Granados, Thomas C. Booth, Sam E. John

    cs.RO · cs.LG

    Endovascular brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This work presents the first demonstration of in vitro autonomous robotic navigation for endovascular BCI access in the cerebral venous system. Soft Actor-Critic controllers were trained in silico for two sequential tasks spanning the right internal jugular vein to the superior sagittal sinus, using...

    arxiv.org/abs/2610.03537 · PDF

  3. 03

    XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation

    Tingting Du, Ziyao Wang, Guoheng Sun, Ang Li

    cs.RO · cs.CV · cs.LG

    World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and action based future prediction does not explicitly address the spatial understanding needed for robot manipulation. Existing efforts often add a limited set of spatial prediction tasks through specialized heads or branches, leaving both the range of spatial supervision and the model architecture...

    arxiv.org/abs/2610.03516 · PDF

  4. 04

    Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models

    Yukiya Horiba, Koshiro Aoki, Shunsuke Yasuki, Bum Jun Kim, Taiki Miyanishi

    cs.RO · cs.AI

    Adversarial patches can disrupt Vision-Language-Action (VLA) models by manipulating visual observations, leading to failures in robot control. However, it remains poorly understood which internal mechanisms underlie these failures and how targeted interventions can mitigate them. In this work, we mechanistically analyze VLA representations using a sparse autoencoder (SAE) and identify a feature whose activation strongly correlates with the...

    arxiv.org/abs/2610.03498 · PDF

  5. 05

    MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation

    Chenzhi Liu, Yue Zhang, Jiehong Lin, Jianan Wang, Bo Wang, Zhongrui Wang, Xiaojuan Qi

    cs.RO · cs.AI

    Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and arm control. While recent Vision-Language-Action models excel at short-horizon tasks, they lack the hierarchical reasoning required for multi-stage objectives. Furthermore, existing hierarchical agents suffer from rigid sub-task mapping, inflexible replanning, and a lack of continuous learning....

    arxiv.org/abs/2610.03476 · PDF

  6. 06

    Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

    Lik Hang Kenny Wong, Yiyao Ma, Xiu-Shen Wei, Zelong Tan, Zhuheng Song, Dongsheng Xie, Kai Chen, Qi Dou

    cs.RO · cs.AI

    Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient policy a key issue. To address this, we propose VISTA, a workspace-level equivariant visuotactile diffusion policy for data-efficient contact-rich imitation learning. VISTA projects visual and tactile observations into spherical tokens, injects tactile contact cues into visual spherical...

    arxiv.org/abs/2610.03333 · PDF

  7. 07

    Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics

    Seunghwan Jang, Jeongyong Yang, Siddharth Ancha, SooJean Han

    cs.RO · cs.LG

    Generative planners based on diffusion/flow matching can learn to synthesize long-horizon trajectories from demonstrations. However, real-world deployment requires (i) enforcing safety constraints during execution and (ii) tight online replanning at fast execution rates. Prior safe diffusion/flow planners generate the agent's full trajectory at once, while repeatedly perturbing intermediate states to satisfy safety constraints. This approach...

    arxiv.org/abs/2610.03132 · PDF

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