IEEE RA-L

PEAfowl: Perception-Enhanced Multi-View Vision-Language-Action for Bimanual Manipulation

PEAfowl：感知增强的多视角视觉-语言-动作用于双臂操作

Qingyu Fan, Zhaoxiang Li, Jinrui Hu, Yi Lu, Wang Chen, Qiu Shen, Xiao-xiao Long, Yinghao Cai, Tao Lu, Shuo Wang, Xun Cao

机构 * Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所） ； Nanjing University（南京大学） 纠错

AI总结 PEAfowl通过增强感知的多视角视觉-语言-动作策略，提升双臂操作在复杂环境中的稳定性和成功率。

Comments Accepted by IEEE Robotics and Automation Letters (RA-L), 2026. This version includes an extended appendix with additional implementation details, deployment analysis, robustness evaluation, and real-world evaluation protocol. DOI: https://doi.org/10.1109/LRA.2026.3726379

Lies We Can Trust: Quantifying Action Uncertainty with Inaccurate Stochastic Dynamics through Conformalized Nonholonomic Lie groups

我们能信任的谎言：通过符合化非守恒李群量化动作不确定性

Luís Marques, Maani Ghaffari, Dmitry Berenson

机构 * Robotics Department, University of Michigan（密歇根大学机器人系） 纠错

AI总结 本文提出CLAPS算法，利用非守恒李群结构，通过符合预测方法在非欧几里得配置空间中量化动作不确定性，提升预测区域的体积效率。

Comments 12 pages, 7 figures, 4 tables. Published in IEEE Robotics and Automation Letters. Extended version with appendix. Project page: this https URL (https://um-arm-lab.github.io/claps/)