cs.RO · 2026-06-14 · No. 23

Robotics, 2026-06-14.

6 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    Mana: Dexterous Manipulation of Articulated Tools

    Zhao-Heng Yin, Guanya Shi, Pieter Abbeel, C. Karen Liu

    cs.RO · cs.AI · cs.CV · cs.LG

    Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim-to-real...

    arxiv.org/abs/2606.13677 · PDF

  2. 02

    Real-Time Execution with Autoregressive Policies

    Sangkyu Lee, Seohyeon Park, Tackgeun You, Avi Caciularu, Idan Szpektor, Hwasup Lim, Youngjae Yu

    cs.RO · cs.AI

    Real-time execution, enabled by asynchronous inference that ensures both smooth action trajectories and fast reactivity, is critical for realistic deployments of large-scale Vision-Language-Action models. However, recent work on real-time execution primarily focuses on variants of diffusion policies, even though it is more critical for autoregressive policies given their slower rollout speed in synchronous inference. In contrast, we...

    arxiv.org/abs/2606.13355 · PDF

  3. 03

    Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes

    Anna-Maria Velentza, Anne-Gwenn Bosser

    cs.RO · cs.AI

    Humor plays a central role in human social relationships, and recent advances in computational humor create new opportunities for integrating humor into human-robot interaction (HRI). While large language models (LLMs) can generate diverse forms of humor, it remains unclear how humor style, joke content, and language preference shape perceptions of robot-delivered humor in group settings. In this exploratory study, we employed a mixed...

    arxiv.org/abs/2606.13256 · PDF

  4. 04

    Proprioceptive-visual correspondence enables self-other distinction in humanoid robots

    Yurun Chen, Tianyuan Gao, Yizhong Ge, Shikun Ban, Yizhou Wang, Hongkai Xiong, Wenjun Zeng, Wentao Zhu

    cs.RO · cs.AI

    Distinguishing self from others is a prerequisite for social intelligence, yet humanoid robots that increasingly share workspaces with humans still lack this ability. Here we show that a humanoid robot can learn self-other distinction from proprioceptive-visual correspondence, without any identity labels or kinematic models. Once established, this distinction bootstraps a predictive self-model that maps joint configurations to...

    arxiv.org/abs/2606.13222 · PDF

  5. 05

    EA-WM: Event-Aware World Models with Task-Specification Grounding for Long-Horizon Manipulation

    Kailin Wang, Haoxiang Jie, Yaoyuan Yan, Jiacheng Zhou, Zhiyou Heng

    cs.RO · cs.AI

    Pretrained-feature world models provide a useful substrate for robot imagination, but visual or latent prediction alone does not determine whether an imagined future satisfies task-relevant events. Long-horizon manipulation requires progress signals that are relational, predicate-level, and physically grounded: whether an object has moved, whether a drawer or contact state has changed, whether a placement predicate is satisfied, and whether a...

    arxiv.org/abs/2606.13053 · PDF

  6. 06

    An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics

    Zhe Liu, Huanbo Jin, Zhaohui Du, Zhe Wang, He Xu, Peijia Li, Jiaming Gu, Quan Lu, Qi Wang, Bin Ji, Ting Xiao

    cs.RO · cs.AI

    Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproducible task generation, open editable laboratory assets, and efficient pipelines that turn limited demonstrations into usable training data. We present Pipette, an embodied simulation platform, benchmark, and data-efficient augmentation framework for wet-lab robot...

    arxiv.org/abs/2606.12936 · PDF

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