机器人 / 具身智能

Toward Maturity-Based Certification of Embodied AI: Quantifying Trustworthiness Through Measurement Mechanisms

迈向基于成熟度的具身AI认证：通过测量机制量化可信度

Michael C. Darling, Alan H. Hesu, Michael A. Mardikes, Brian C. McGuigan, Reed M. Milewicz

机构 * Michael C. Darling ； Alan H. Hesu ； Michael A. Mardikes ； Brian C. McGuigan ； Reed M. Milewicz 纠错

专题命中 机器人数据与评测 ：embodied AI(title,abstract)；分类 cs.AI、cs.LG

AI总结 本文提出基于成熟度的具身AI认证框架，通过量化机制评估可信度，并通过无人机检测案例验证其可行性。

Comments Accepted to AAAI-26 Bridge Program B10: Making Embodied AI Reliable with Testing and Formal Verification

$π_0$: A Vision-Language-Action Flow Model for General Robot Control

π₀：一种面向通用机器人控制的视觉-语言-动作流模型

Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, Ury Zhilinsky

机构 * Physical Intelligence 纠错

专题命中 机器人数据与评测 ：robot learning(abstract)；robot foundation model(abstract)；分类 cs.RO、cs.LG

AI总结 本文提出了一种基于预训练视觉-语言模型的流匹配架构，用于通用机器人控制，通过零样本学习和微调实现多样任务的执行。

Comments See project website for videos: https://physicalintelligence.company/blog/pi0 Published in RSS 2025

The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms

RoboSense挑战：感知一切，导航任何地方，跨平台适应

Lingdong Kong, Shaoyuan Xie, Zeying Gong, Ye Li, Meng Chu, Ao Liang, Yuhao Dong, Tianshuai Hu, Ronghe Qiu, Rong Li, Hanjiang Hu, Dongyue Lu, Wei Yin, Wenhao Ding, Linfeng Li, Hang Song, Wenwei Zhang, Yuexin Ma, Junwei Liang, Zhedong Zheng, Lai Xing Ng, Benoit R. Cottereau, Wei Tsang Ooi, Ziwei Liu, Zhanpeng Zhang, Weichao Qiu, Wei Zhang, Ji Ao, Jiangpeng Zheng, Siyu Wang, Guang Yang, Zihao Zhang, Yu Zhong, Enzhu Gao, Xinhan Zheng, Xueting Wang, Shouming Li, Yunkai Gao, Siming Lan, Mingfei Han, Xing Hu, Dusan Malic, Christian Fruhwirth-Reisinger, Alexander Prutsch, Wei Lin, Samuel Schulter, Horst Possegger, Linfeng Li, Jian Zhao, Zepeng Yang, Yuhang Song, Bojun Lin, Tianle Zhang, Yuchen Yuan, Chi Zhang, Xuelong Li, Youngseok Kim, Sihwan Hwang, Hyeonjun Jeong, Aodi Wu, Xubo Luo, Erjia Xiao, Lingfeng Zhang, Yingbo Tang, Hao Cheng, Renjing Xu, Wenbo Ding, Lei Zhou, Long Chen, Hangjun Ye, Xiaoshuai Hao, Shuangzhi Li, Junlong Shen, Xingyu Li, Hao Ruan, Jinliang Lin, Zhiming Luo, Yu Zang, Cheng Wang, Hanshi Wang, Xijie Gong, Yixiang Yang, Qianli Ma, Zhipeng Zhang, Wenxiang Shi, Jingmeng Zhou, Weijun Zeng, Kexin Xu, Yuchen Zhang, Haoxiang Fu, Ruibin Hu, Yanbiao Ma, Xiyan Feng, Wenbo Zhang, Lu Zhang, Yunzhi Zhuge, Huchuan Lu, You He, Seungjun Yu, Junsung Park, Youngsun Lim, Hyunjung Shim, Faduo Liang, Zihang Wang, Yiming Peng, Guanyu Zong, Xu Li, Binghao Wang, Hao Wei, Yongxin Ma, Yunke Shi, Shuaipeng Liu, Dong Kong, Yongchun Lin, Huitong Yang, Liang Lei, Haoang Li, Xinliang Zhang, Zhiyong Wang, Xiaofeng Wang, Yuxia Fu, Yadan Luo, Djamahl Etchegaray, Yang Li, Congfei Li, Yuxiang Sun, Wenkai Zhu, Wang Xu, Linru Li, Longjie Liao, Jun Yan, Benwu Wang, Xueliang Ren, Xiaoyu Yue, Jixian Zheng, Jinfeng Wu, Shurui Qin, Wei Cong, Yao He

机构 * Technical Committee（技术委员会） ； Challenge & Workshop Organizers（挑战与研讨会组织者） 纠错

专题命中 机器人数据与评测 ：navigation(abstract)；分类 cs.RO

AI总结 RoboSense 2025挑战通过统一五个研究领域，推动机器人感知在多样化场景下的鲁棒性和适应性，提升真实环境中的感知可靠性与行动稳健性。

Comments Official IROS 2025 RoboSense Challenge Report; 51 pages, 37 figures, 5 tables; Competition Website at https://robosense2025.github.io/

PEAR: Planner-Executor Agent Robustness Benchmark

PEAR：规划-执行代理鲁棒性基准

Shen Dong, Mingxuan Zhang, Pengfei He, Li Ma, Bhavani Thuraisingham, Hui Liu, Yue Xing

机构 * Michigan State University（密歇根州立大学） ； Purdue University（普渡大学） ； University of Texas at Dallas（德克萨斯大学达拉斯分校） 纠错

专题命中 机器人数据与评测 ：manipulation(abstract)；分类 cs.LG

AI总结 PEAR基准通过评估规划-执行多智能体系统的鲁棒性，揭示了规划器弱点对任务性能的影响及鲁棒性与性能的权衡。

Comments arXiv admin note: This submission has been withdrawn by arXiv administrators due to incorrect authorship. Author list truncated

An Improved Dual-Attention Transformer-LSTM for Small-Sample Prediction of Modal Frequency and Actual Anchor Radius in Micro Hemispherical Resonator Design

一种改进的双注意Transformer-LSTM用于微球形谐振器设计中少量样本的模态频率和实际锚半径预测

Yuyi Yao, Gongliu Yang, Runzhuo Xu, Yongqiang Tu, Haozhou Mo

专题命中 机器人数据与评测 ：navigation(abstract)

AI总结 本文提出改进的Transformer-LSTM模型，用于快速预测微球形谐振器设计中的模态频率和实际锚半径，提升设计效率和精度。

Comments Due to the fact that the results of this article are from simulation experiments and there is a certain gap with the actual experimental results, this article has not been corrected. Therefore, the authors Yang and Tu have not given final consent to this submitted version, nor have they authorized the submitter to publish this public preprint