IROS

Experimental Comparison of Whole-Body Control Formulations for Humanoid Robots in Task Acceleration and Task Force Spaces

人形机器人在任务加速空间和任务力空间中完整体控制方案的实验比较

Sait Sovukluk, Grazia Zambella, Tobias Egle, Christian Ott

机构 * Automation and Control Inst. (ACIN), TU Wien（自动化与控制研究所（ACIN），维也纳技术大学） ； Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（机器人与机电研究所，德国航空航天中心（DLR）） 纠错

AI总结 本文通过实验比较了人形机器人在任务加速空间和任务力空间中两种完整体控制方案的性能差异，分析了其在不同任务中的鲁棒性及优缺点。

Comments This paper has been accepted for publication in 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025). - Link to video: https://youtu.be/Nfm50ycz-FU

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks

让您的水下无人航行器适应：一种环境感知的强化学习框架用于水下任务

Yimian Ding, Jingzehua Xu, Guanwen Xie, Shuai Zhang, Yi Li

AI总结 本文提出一种环境感知的强化学习框架，通过动态捕捉流场数据和利用大语言模型优化，提升水下AUV的适应性和任务性能。

Comments This paper has been accepted by IROS 2025. Yimian Ding and Jingzehua Xu contributed equally to this work, and Jingzehua Xu is also the corresponding author of this paper

TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion

TAR：通过对比学习实现的教师对齐表示用于四足运动

Amr Mousa, Neil Karavis, Michele Caprio, Wei Pan, Richard Allmendinger

机构 * University of Manchester（曼彻斯特大学） ； BAE Systems（BAE系统公司） 纠错

AI总结 TAR通过对比学习实现教师对齐表示，提升四足运动在现实世界中的泛化能力和适应性。

Comments This work has been accepted for publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025

Journal ref 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China, 2025, pp. 11669-11676

AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment

AssistantX: 一种基于大语言模型的协作人类 populated 环境中的主动助手

Nan Sun, Bo Mao, Yongchang Li, Di Guo, Huaping Liu

机构 * Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学） ； School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学） 纠错

AI总结 AssistantX 是一种基于大语言模型的主动助手，通过多代理框架实现自主操作，具备高级推理能力和协作意识，能有效响应用户指令并主动寻求帮助以完成任务。

Comments 8 pages, 10 figures, 6 tables

Journal ref 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China, 2025