arXivDaily arXiv每日学术速递 周一至周五更新

期刊&会议

International Conference on Intelligent Robots and Systems · 会议 · Robotics

2026-07-07 至 2026-07-07 共收录 4
2606.28720 2026-07-07 cs.RO 版本更新

CubifyGS: Object-Centric 3D Gaussian Splatting for Lifelong Dynamic Scene Maintenance

CubifyGS: 面向对象的3D高斯泼溅用于终身动态场景维护

Bohan Ren, Dianyi Yang, Shiyang Liu, Yu Gao, Jiadong Tang, Zhilin Lai, Yi Yang, Mengyin Fu

机构 * School of Automation, Beijing Institute of Technology, Beijing, China(北京理工大学自动化学院,北京,中国) Guangzhou Saite Intelligent Technology Co., Ltd.(广州赛泰智能科技有限公司)

AI总结 提出CubifyGS,一种面向对象的映射框架,通过将可移动实例建模为可重用高斯资产,并采用事件触发自适应优化,实现刚性物体重排下的高效动态场景维护。

Comments Accepted to IROS 2026. 8 pages, 5 figures, 4 tables

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2604.21241 2026-07-07 cs.RO cs.AI 版本更新

CorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors

CorridorVLA:通过稀疏锚点实现生成动作头的显式空间约束

Dachong Li, ZhuangZhuang Chen, Jin Zhang, Jianqiang Li

机构 * College of Computer Science and Software Engineering(计算机科学与软件工程学院) National Engineering Laboratory for Big Data System Computing Technology(大数据系统计算技术国家工程实验室)

AI总结 CorridorVLA通过稀疏锚点提供显式空间约束,提升动作生成性能,在LIBERO-Plus基准上改进成功率3.4%-12.4%。

Comments Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)

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2603.20659 2026-07-07 cs.RO 版本更新

StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models

StageCraft: 通过执行意识缓解VLA模型中干扰和障碍故障

Kartikay Milind Pangaonkar, Prabin Rath, Omkar Patil, Nakul Gopalan

机构 * Arizona State University(亚利桑那州立大学)

AI总结 StageCraft通过利用大规模视觉语言模型进行推理,改进预训练VLA策略性能,通过操控环境初始状态避免执行故障,实现在三个真实任务领域中性能提升40%。

Comments Accepted to IEEE International Conference on Intelligent Robots and Systems (IROS) 2026

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2509.15061 2026-07-07 cs.RO cs.CV 版本更新

Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue

Ask-to-Clarify: 通过多轮对话解决指令歧义

Xingyao Lin, Xinghao Zhu, Tianyi Lu, Guojin Zhong, Sicheng Xie, Hui Zhang, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang

机构 * College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China(复旦大学计算机科学与人工智能学院) Shanghai Innovation Institute, Shanghai, China(上海创新研究院) Mechanical Systems Control Lab, UC Berkeley, California, USA(伯克利机械系统控制实验室)

AI总结 本文提出Ask-to-Clarify框架,通过多轮对话解决指令歧义问题,结合视觉语言模型和扩散模型,采用两阶段知识绝缘策略训练,实现多任务中更高效的协作式具身代理。

Comments Accepted by IROS 2026

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