cs.RO · 2026-06-17 · No. 26

Robotics, 2026-06-17.

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

    Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement

    Mingtong Zhang, Dhruv Shah

    cs.RO · cs.AI

    Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we propose VERITAS, a generator-verifier framework for generalist robot policies for inference-time policy steering and self-improvement. We use a pre-trained generalist robot policy as a ``generator'' and pair it with a gradient-free ``visual verifier'' that evaluates...

    arxiv.org/abs/2606.18247 · PDF

  2. 02

    EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning

    Wanhao Niu, Qiyan Ke, Yuan Sun, Hao Sun, Jie Xu, Muyuan Ma, Ruiqi Hu, Fuchun Sun

    cs.RO · cs.AI

    Cross-end-effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors. Existing grasp generators are typically designed for a fixed embodiment or encode embodiment identity with a static descriptor, which weakens transfer when topology, actuation coupling, and contact geometry differ substantially. We present EAGG, an embodiment-aligned grasp...

    arxiv.org/abs/2606.18092 · PDF

  3. 03

    Uncertainty Quantification for Flow-Based Vision-Language-Action Models

    Ralf Römer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig

    cs.RO · cs.LG

    Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable. This presents a critical limitation for real-world deployment in non-stationary...

    arxiv.org/abs/2606.18043 · PDF

  4. 04

    PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

    Bochen Yang, Lianlei Shan

    cs.RO · cs.AI

    Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation. Directly decoding actions from vision-language backbone representations enables low-latency control, whereas explicit reasoning through textual chains, pixel-level subgoals, or action search can improve planning but incurs substantial latency and computational cost. We propose PearlVLA, a VLA framework that moves...

    arxiv.org/abs/2606.17924 · PDF

  5. 05

    Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models

    Haoqi Yuan, Zhixuan Liang, Anzhe Chen, Ye Wang, Haoyang Li, Pei Lin, Yiyang Huang, Zixing Lei, Tong Zhang, Jiazhao...

    cs.RO · cs.CV · cs.LG

    Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale. In this report, we investigate whether this scaling recipe can be applied to robotic manipulation to achieve genuine generalization. This is challenging because, unlike text, manipulation data is heterogeneous by nature, expensive to collect, and narrow in diversity, making alignment...

    arxiv.org/abs/2606.17846 · PDF

  6. 06

    ED3R: Energy-Aware Distributed Disaster Detection Enabled by Cooperative Robotic Agents

    Lina Magoula, Nikolaos Koursioumpas, Nancy Alonistioti, Ramin Khalili

    cs.RO · cs.AI · cs.CV · cs.MA

    Robotics are expected to support environmental monitoring and natural disaster management, where decisions must be made under uncertainty, resource limitations, and strict operational constraints. In critical missions, such as wildfires, robotic agents must not only identify hazardous events with sufficient confidence, but also manage the energy cost and time until detection. This paper introduces ED3R, an energy-aware distributed framework...

    arxiv.org/abs/2606.17739 · PDF

  7. 07

    MagicSim: A Unified Infrastructure for Executable Embodied Interaction

    Haoran Lu, Songling Liu, Yue Chen, Guo Ye, Mutian Shen, Shuyang Yu, Yu Xiao, Jihai Zhao, Shang Wu, Jianshu Zhang,...

    cs.RO · cs.AI · cs.CV

    Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment. Existing pipelines split these layers with "magic" actions, disconnected training environments, or forward-only renders that cannot reproduce, evaluate, and annotate the same episode. We present MagicSim, an embodied interaction...

    arxiv.org/abs/2606.17511 · PDF

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