6 embodied-AI and robotics papers accepted at ICLR 2025, 5 with code.6 篇被 ICLR 2025 接收的具身智能与机器人论文，其中 5 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。
DPPO — fine-tunes a pre-trained diffusion policy with PPO by treating the denoising steps as part of the MDP; reports stable gains over other RL fine-tuning methods and sim-to-real transfer.DPPO：把去噪步骤视为 MDP 的一部分，用 PPO 微调预训练扩散策略；报告称比其他强化学习微调方法更稳定，并实现了仿真到真机迁移。
@misc{ren2024diffusion,
title = {{Diffusion Policy Policy Optimization}},
author = {Allen Z. Ren and Justin Lidard and Lars L. Ankile and Anthony Simeonov and Pulkit Agrawal and Anirudha Majumdar and Benjamin Burchfiel and Hongkai Dai and Max Simchowitz},
year = {2024},
eprint = {2409.00588},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.00588},
}
A hierarchical VLA in which a fine-tuned VLM draws a coarse 2D path for the end effector on the image and a low-level 3D policy follows it, so the VLM can be trained on cheap off-domain data such as sim or videos.分层 VLA：微调后的 VLM 在图像上画出末端执行器的粗略二维路径，再由底层三维策略跟随执行；因此 VLM 可以用仿真、视频等低成本的非本域数据训练。
@misc{li2025hamster,
title = {{HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation}},
author = {Yi Li and Yuquan Deng and Jesse Zhang and Joel Jang and Marius Memmel and Raymond Yu and Caelan Reed Garrett and Fabio Ramos and Dieter Fox and Anqi Li and Abhishek Gupta and Ankit Goyal},
year = {2025},
eprint = {2502.05485},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.05485},
}
LAPA pretrains a VLA without action labels: it learns discrete latent actions between video frames, predicts them from image + text, then maps them to robot actions; reports beating OpenVLA on real tasks.LAPA 无需动作标注即可预训练 VLA：先从视频相邻帧间学习离散隐动作，再训练模型由图像和指令预测隐动作，最后用少量机器人数据映射到真实动作；报告在真机任务上优于 OpenVLA。
@misc{ye2024latent,
title = {{Latent Action Pretraining from Videos}},
author = {Seonghyeon Ye and Joel Jang and Byeongguk Jeon and Sejune Joo and Jianwei Yang and Baolin Peng and Ajay Mandlekar and Reuben Tan and Yu-Wei Chao and Bill Yuchen Lin and Lars Liden and Kimin Lee and Jianfeng Gao and Luke Zettlemoyer and Dieter Fox and Minjoon Seo},
year = {2024},
eprint = {2410.11758},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.11758},
}
Seer forecasts the next visual state and feeds it to an inverse dynamics model that outputs actions, trained end to end and pretrained on DROID; reports 4.28 avg. length on CALVIN ABC-D plus LIBERO and real-robot gains.Seer 先预测未来视觉状态，再由逆动力学模型据此输出动作，端到端训练并在 DROID 上预训练；报告 CALVIN ABC-D 平均任务长度 4.28，在 LIBERO-LONG 和真机任务上也有提升。
@misc{tian2024predictive,
title = {{Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation}},
author = {Yang Tian and Sizhe Yang and Jia Zeng and Ping Wang and Dahua Lin and Hao Dong and Jiangmiao Pang},
year = {2024},
eprint = {2412.15109},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.15109},
}
Extends SAM to video with a streaming memory, so a mask prompted once is tracked across frames — handy for following objects during manipulation.用流式记忆把 SAM 扩展到视频：一次提示得到的掩码可在后续帧中持续跟踪，适合在操作过程中跟随目标物体。
@misc{ravi2024sam,
title = {{SAM 2: Segment Anything in Images and Videos}},
author = {Nikhila Ravi and Valentin Gabeur and Yuan-Ting Hu and Ronghang Hu and Chaitanya Ryali and Tengyu Ma and Haitham Khedr and Roman Rädle and Chloe Rolland and Laura Gustafson and Eric Mintun and Junting Pan and Kalyan Vasudev Alwala and Nicolas Carion and Chao-Yuan Wu and Ross Girshick and Piotr Dollár and Christoph Feichtenhofer},
year = {2024},
eprint = {2408.00714},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2408.00714},
}
Draws the robot's recent motion traces on the input image ('visual trace prompting') and fine-tunes OpenVLA on 150K trajectories; reports ~10% gains on SimplerEnv and 3.5x on real WidowX tasks; 4B variant too.把机器人近期的运动轨迹画在输入图像上（“视觉轨迹提示”），并在 15 万条轨迹上微调 OpenVLA；报告在 SimplerEnv 上提升约 10%、真实 WidowX 任务上提升 3.5 倍，另有基于 Phi-3-Vision 的 4B 精简版。
@misc{zheng2024tracevla,
title = {{TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies}},
author = {Ruijie Zheng and Yongyuan Liang and Shuaiyi Huang and Jianfeng Gao and Hal Daumé and Andrey Kolobov and Furong Huang and Jianwei Yang},
year = {2024},
eprint = {2412.10345},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.10345},
}