cs.RO · 2026-08-17 · No. 87

Robotics, 2026-08-17.

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

    Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes

    Alexei Odinokov, Rostislav Yavorskiy

    cs.RO · cs.AI

    As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challenge. We present a unified framework that bridges systematic hazard analysis and runtime enforcement using hazard-informed safety envelopes. Rather than treating safety as a static constraint isolated within individual software modules, we introduce a cross-layer safety transformation process...

    arxiv.org/abs/2608.14481 · PDF

  2. 02

    Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration

    Ajith Anil Meera, Pablo Lanillos, Wouter Kouw

    cs.RO · cs.IT · cs.LG

    An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classical information-seeking and reward-seeking criteria address only one of these objectives at a time. Here, we propose Expected...

    arxiv.org/abs/2608.14466 · PDF

  3. 03

    Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation

    Yuxuan Chen, Wanruo Zhang, Xiao Li

    cs.RO · cs.AI

    Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation. However, existing benchmarks mainly evaluate generalization on static manipulation tasks and largely overlook dynamic interaction scenarios. To address this gap, we present ReflexBench, a benchmark for reaction-critical manipulation. ReflexBench contains six dynamic tasks and introduces an evaluation framework that decouples simulator...

    arxiv.org/abs/2608.14379 · PDF

  4. 04

    CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving

    Anisa Saleem, Duksu Kim

    cs.RO · cs.LG

    Reinforcement learning is promising for autonomous urban driving, but long-horizon goal-directed navigation asks a policy to acquire several competing behaviors at once--reaching a distant goal, tracking a route, avoiding obstacles, obeying signals--and a fixed objective gives no order in which to learn them. This paper presents CORAL, which advances two schedules together: a five-stage curriculum that progressively lengthens routes and...

    arxiv.org/abs/2608.14332 · PDF

  5. 05

    AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning

    Wenhao Tang, Tianyang Chen, Zhejun Cui, Boyuan An, Jiayu Chen, Ruize Zhang, Huidong Liu, Tianyue Wu, Qingmin Liao,...

    cs.RO · cs.LG

    Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors. Traditional rule-based or differential-game approaches often struggle with high-dimensional aerial interactions and agile maneuvering. We present AgilePE, a complete system for autonomous UAV pursuit-evasion via self-play reinforcement learning....

    arxiv.org/abs/2608.14135 · PDF

  6. 06

    Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use

    Yi Ding, Yanzhao Yu, Xili Dai, Xianbiao Qi, Peiwen Sun, Xueqian Wang, Xiangyu Yue, Jianan Wang

    cs.RO · cs.AI · cs.CV

    This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART). ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. Compared to vanilla VLA models with a whole continuous action solution space, ART reduces the complexity of the action solution space...

    arxiv.org/abs/2608.14047 · PDF

  7. 07

    AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning

    Zhiyue Zhao, Jingyi Wu, Hairuo Liu, Mingyu Liu, Liyang Li, Hengdi Zhang, Tong He, Zhengxue Cheng

    cs.RO · cs.AI

    Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spaces vary across embodiments. Policies trained on heterogeneous data can also entangle task-relevant visual cues with embodiment-specific appearance, limiting cross-embodiment generalization. We present AdvDex, a unified Vision-Language-Action framework for learning...

    arxiv.org/abs/2608.14028 · PDF

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