cs.RO · 2026-08-03 · No. 73

Robotics, 2026-08-03.

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

    FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution

    Peize Li, Ruimeng Zhang, Ru Zhang, Cong Huang, Kai Chen, Shanghang Zhang

    cs.RO · cs.AI · eess.SY

    Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations--not recursive rollout. Existing WAMs address this by refreshing history or KV cache with ground-truth data between chunks. However, such chunk-wise feedback operates at a coarse temporal granularity and thus fails to correct prediction errors at the...

    arxiv.org/abs/2607.29235 · PDF

  2. 02

    CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning

    Yuxin Chen, Hari Srikanth, Nathan Jew, Menglin Wu, Pengcheng Wang, Junli Ren, Masayoshi Tomizuka, Peng Xu, Jinyu...

    cs.RO · cs.AI

    While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users' ability to adapt them to new tasks, embodiments, and deployment settings. Following the LLM community, an emerging access paradigm for closed-weight robot foundation models is the managed supervised fine-tuning (SFT) API, where users submit training data and receive...

    arxiv.org/abs/2607.29172 · PDF

  3. 03

    ActFovea: Runtime Safeguarding for VLA Policies via Spatiotemporal Visual-Action Consistency

    Wenda Yu, Tianshi Wang, Fengling Li, Xin Li, Jingjing Li, Lei Zhu

    cs.RO · cs.AI

    Vision-language-action (VLA) policies achieve strong performance in robotic manipulation but remain vulnerable to runtime disturbances that break the temporal alignment among visual observations, robot states, and executed actions. We introduce ActFovea, a plug-and-play safeguarding framework that detects and mitigates such failures without retraining or modifying the underlying VLA policy. ActFovea uses robot kinematics, proprioceptive...

    arxiv.org/abs/2607.29169 · PDF

  4. 04

    Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving

    Jiwei Yang, Zhengxian Chen, Chaosheng Huang, Jun Li

    cs.RO · cs.AI

    Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion. We argue that planning need not reconstruct the complete future world, but only focus on scene features that affect future ego action. Based on this perspective, we propose Auto-JEPA, an action-oriented latent world model that learns continuous future driving intent through joint-embedding...

    arxiv.org/abs/2607.29031 · PDF

  5. 05

    PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball

    Lizhi Yang, Junheng Li, Aaron D. Ames

    cs.RO · cs.AI

    We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body humanoid dodgeball. The deployed policy sees the ball only as segmentation-masked depth from a head-mounted camera, while training-time CBF guidance represents clearance to every body link, and an adversarial motion prior regularizes the resulting evasive reflexes. We evaluate on a controlled...

    arxiv.org/abs/2607.28623 · PDF

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