3 embodied-AI and robotics papers accepted at CVPR 2025, 3 with code.3 篇被 CVPR 2025 接收的具身智能与机器人论文，其中 3 篇附代码。
Feed papers are listed when their arXiv comment says they were accepted (e.g. 'Accepted to CoRL 2026'); papers that do not say so are missing, and the list grows as authors post camera-ready versions. Curated entries carry a hand-checked venue.日常 arXiv 论文仅在其 arXiv 备注写明已被接收（如 “Accepted to CoRL 2026”）时列入；未注明的论文不会出现，列表会随作者上传终稿而增加。精选条目的会议/期刊信息经过人工核对。
Stereo depth model trained on large synthetic data that reports strong zero-shot accuracy on real scenes — a drop-in depth source for stereo-camera robots.在大规模合成数据上训练的双目深度模型，报告称在真实场景上零样本精度很强；可直接作为双目相机机器人的深度来源。
@misc{wen2025foundationstereo,
title = {{FoundationStereo: Zero-Shot Stereo Matching}},
author = {Bowen Wen and Matthew Trepte and Joseph Aribido and Jan Kautz and Orazio Gallo and Stan Birchfield},
year = {2025},
eprint = {2501.09898},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2501.09898},
}
Isaac Sim platform where an LLM builds tasks and layouts from a task-oriented scene graph over ~10K objects; its 200-scenario benchmark finds modular foundation-model systems generalize better than end-to-end policies.基于 Isaac Sim 的平台：LLM 依据任务导向场景图，从约 1 万个物体中生成任务与布局；其 200 个场景的基准发现，结合基础模型的模块化系统比端到端策略泛化更好。
@misc{gao2025genmanip,
title = {{GenManip: LLM-driven Simulation for Generalizable Instruction-Following Manipulation}},
author = {Ning Gao and Yilun Chen and Shuai Yang and Xinyi Chen and Yang Tian and Hao Li and Haifeng Huang and Hanqing Wang and Tai Wang and Jiangmiao Pang},
year = {2025},
eprint = {2506.10966},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.10966},
}
One model for UI navigation and robot manipulation, pretrained on images, videos and robot data labelled with Set-of-Mark (what to act on) and Trace-of-Mark (how things move); model and code are public.同一个模型同时做界面操作与机器人操作：在图像、视频和机器人数据上预训练，用 Set-of-Mark 标注可操作对象、用 Trace-of-Mark 标注运动轨迹；模型与代码公开。
@misc{yang2025magma,
title = {{Magma: A Foundation Model for Multimodal AI Agents}},
author = {Jianwei Yang and Reuben Tan and Qianhui Wu and Ruijie Zheng and Baolin Peng and Yongyuan Liang and Yu Gu and Mu Cai and Seonghyeon Ye and Joel Jang and Yuquan Deng and Lars Liden and Jianfeng Gao},
year = {2025},
eprint = {2502.13130},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2502.13130},
}