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
3 curated papers, code, datasets and hardware from National University of Singapore (1 with code), with one-line takeaways.3 个来自新加坡国立大学的精选论文、代码、数据集与硬件（1 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。
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},
}
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},
}