cs.RO · 2026-05-22 · No. 7

Robotics, 2026-05-22.

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

    Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning

    Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier, Davide Scaramuzza

    cs.RO · cs.AI · cs.LG · cs.MA

    Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the dominant single-agent paradigm for physical applications, where other actors are ignored or treated as environmental noise, preventing effective coordination. Here we show that multi-agent reinforcement learning provides the essential safety scaffolding required for...

    arxiv.org/abs/2605.22748 · PDF

  2. 02

    Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments

    Hoang-Dung Bui, Abhish Khanal, Raihan Islam Arnob, Gregory J. Stein

    cs.RO · cs.AI

    Autonomous robot teams navigating partially known environments face costly backtracking when ground robots encounter blocked roads that are only revealed upon physical traversal. We address this with Scout-Assisted Planning, a heterogeneous planning framework in which scouting Unmanned Aerial Vehicles proactively gather environmental information to improve Unmanned Ground Vehicle navigation. To focus scouting on the most consequential edges,...

    arxiv.org/abs/2605.22693 · PDF

  3. 03

    Steins;Gate Drive: Semantic Safety Arbitration over Structured Futures for Latency-Decoupled LLM Planning

    Anjie Qiu, Hans D. Schotten

    cs.RO · cs.AI

    Cloud-hosted LLM driver agents provide useful semantic judgments, but their inference latency exceeds stepwise vehicle-control windows. Learned world models predict futures, but they usually keep future generation and action selection inside large coupled loops. We present SteinsGateDrive, a latency-decoupled planner-runtime architecture in which the worldline metaphor from the eponymous story names one plausible consequence of an...

    arxiv.org/abs/2605.22456 · PDF

  4. 04

    Action with Visual Primitives

    Weilong Guo, Yuchen Wang, Renping Zhou, Yunfeng Zhang, Rui Fang, Yue Meng, Wenda Xu, Yuan He, Gao Huang

    cs.RO · cs.AI

    Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation. A common design in current architectures maps language instructions and visual observations to actions in a single forward pass. While conceptually simple, this formulation entangles instruction comprehension, spatial scene understanding, and motor control within a single learning objective. As a result, the action expert must...

    arxiv.org/abs/2605.22183 · PDF

  5. 05

    EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control

    Chushan Zhang, Ruihan Lu, Jinguang Tong, Xuesong Li, Yikai Wang, Hongdong Li

    cs.RO · cs.AI

    Chunked vision-language-action (VLA) policies predict multi-step robot controls, conditioning each update on the current visual observation alone. Yet robot actions cause contact, occlusion, and object motion, and the geometry that later decisions depend on can change before the next visual update arrives. Spatial VLAs improve current-frame geometry. Temporal VLAs aggregate past frames. Neither maintains an action-updated scene prior across...

    arxiv.org/abs/2605.21862 · PDF

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