National University of Singapore · embodied AI & robot learning新加坡国立大学 · 具身智能与机器人学习
7 curated papers, code, datasets and hardware from National University of Singapore (2 with code), with one-line takeaways.7 个来自新加坡国立大学的精选论文、代码、数据集与硬件（2 个附代码），均配一句话要点。
Labs are matched from each entry's affiliation field by simple name rules (data/labs.yaml); joint work appears under every lab named. Only curated entries are listed, because daily arXiv papers carry no affiliations.机构由每个条目的机构字段按名称规则匹配（data/labs.yaml），合作工作会出现在所有署名机构下。仅列出精选条目，因为每日 arXiv 论文不含机构信息。
Separates WAMs from world models, video generators and VLAs, then sorts work by what is generated (rendered, latent or none) and how it is built; concludes the field is moving toward generating less of the future.厘清 WAM 与世界模型、视频生成模型、VLA 的区别，并按生成内容（渲染、潜空间或不生成）和架构分类；结论是领域正走向少生成未来，只保留控制所需。
@misc{shen2026world,
title = {{World Action Models: A Survey}},
author = {Qiuhong Shen and Shihua Zhang and Yue Liao and Qi Li and Zhenxiong Tan and Shizun Wang and Shuicheng Yan and Xinchao Wang},
year = {2026},
eprint = {2606.20781},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.20781},
}
Robot-free dexterous demos with shared contact geometry, fingertip RGB and contact-first retargeting. Reports at least 50 points higher final-stage success than baselines on LEAP and Wuji hands; hardware is open.免机器人灵巧演示框架：共享接触几何、指尖 RGB 视角、接触对齐优先的重定向，加上预测接触面轨迹的层次策略。报告在 LEAP 和 Wuji 手的六组手-任务上，最终阶段成功率比基线至少高 50 个百分点；硬件与软件公开。
@misc{xu2026contact,
title = {{Contact Imitation Interface: Transferring Dexterous Skills by Showing How Contact Unfolds}},
author = {Zhixuan Xu and Yichen Li and Bingchen Wu and Rui Wang and Yiwen Hou and Zixuan Liu and Tianyu Qiu and Lin Shao},
year = {2026},
eprint = {2610.41028},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41028},
}
Survey framing computer-use and robot agents with five functions — perceive, anticipate, plan, act, verify; across 64 benchmarks it finds only 5 test anticipation and 3 test verification. Companion paper list.用"感知、预判、规划、执行、验证"五种能力统一看待电脑操作智能体和机器人智能体的综述；统计 64 个基准后发现只有 5 个评测预判、3 个评测验证。附配套论文列表。
@misc{chen2026multimodal,
title = {{Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use}},
author = {Yanzhe Chen and Ziyi Yang and Jifeng Zhu and Qiming Huang and Ruihe An and Peiyao Xu and Hesen Yang and Runda Liu and Chang Gong and Zhijun Cao and Zechen Bai and Wenzheng Zeng and Yiqi Lin and Guoqiang Liang and Kevin Yuchen Ma and Kevin Qinghong Lin and Mike Zheng Shou},
year = {2026},
eprint = {2610.30108},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30108},
}
Represents object motion as dense 3D trajectories over the whole surface, occluded parts included, for rigid and deformable objects. Adds a 1,210-hour flow dataset and a world model predicting it from image + task.把物体运动表示为覆盖整个表面（含遮挡部分）的稠密 3D 轨迹，适用于刚体与可形变物体。发布 1210 小时的全物体流数据集，并训练从图像和任务描述预测该流的世界模型。
@misc{chen2026objectworld,
title = {{ObjectWorld: Scaling Object-Centric World Models with A Large-Scale 3D Full-Object Flow Dataset}},
author = {Haonan Chen and Sicheng Isabella Lu and Haitian Li and Jiajin Wu and Hexiang Wei and Jiawei Zhang and Xin Fei and Yiwen Hou and Chenrui Tie and Chongkai Gao and Sheng Wang and Lin Shao},
year = {2026},
eprint = {2611.11085},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11085},
}
Survey of recursive self-improvement in robot learning — improving skills, then the process that makes them — and when gains carry forward; notes no physical study has yet isolated gains from recursion itself.把"递归自我改进"放到机器人学习语境下的综述：从提升技能到改进产生技能的过程，并总结增益能持续累积的条件；指出现有真机研究尚未单独验证"递归"本身带来的收益。附论文列表。
@misc{luo2026toward,
title = {{Toward Recursive Self-Improvement in Physical AI}},
author = {Meng Luo and Zechen Bai and Shanqing Xu and Yanlin Li and Kaixin Li and Mengchen Qian and Weifeng Lu and Ran Zhang and Yingtian Zou and Ziyang Luo and Wei Chen and Hongzhan Lin},
year = {2026},
eprint = {2610.29677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29677},
}
Genie Envisioner (AgiBot) — a video-diffusion world model (GE-Base) plus a flow-matching action decoder (GE-Act), an action-conditioned neural simulator (GE-Sim) and the EWMBench benchmark in one platform.智元 Genie Envisioner：一个平台集成视频扩散世界模型 GE-Base、流匹配动作解码器 GE-Act、动作条件神经仿真器 GE-Sim，以及世界模型评测基准 EWMBench。
@misc{liao2025genie,
title = {{Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation}},
author = {Yue Liao and Pengfei Zhou and Siyuan Huang and Donglin Yang and Shengcong Chen and Yuxin Jiang and Yue Hu and Jingbin Cai and Si Liu and Jianlan Luo and Liliang Chen and Shuicheng Yan and Maoqing Yao and Guanghui Ren},
year = {2025},
eprint = {2508.05635},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.05635},
}
Survey of over three hundred studies with a taxonomy of autoregression, diffusion, reinforcement, hybrid and specialized VLAs, plus the datasets, benchmarks and simulators used to test them.综述三百多项研究，把 VLA 分为自回归、扩散、强化学习、混合和专用几类，并介绍常用数据集、评测基准和仿真平台。
@misc{zhang2025pure,
title = {{Pure Vision Language Action (VLA) Models: A Comprehensive Survey}},
author = {Dapeng Zhang and Jing Sun and Chenghui Hu and Xiaoyan Wu and Zhenlong Yuan and Rui Zhou and Fei Shen and Qingguo Zhou},
year = {2025},
eprint = {2509.19012},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2509.19012},
}