6 embodied-AI and robotics papers accepted at ICLR 2026, 6 with code.6 篇被 ICLR 2026 接收的具身智能与机器人论文，其中 6 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。
829 hours of egocentric Apple Vision Pro video with tracked 3D poses for every hand joint, across 194 tabletop tasks from tying shoelaces to folding laundry; includes hand-trajectory prediction benchmarks.用 Apple Vision Pro 采集的 829 小时第一视角视频，配有每个手部关节的三维姿态，覆盖从系鞋带到叠衣服的 194 种桌面任务，并提供手部轨迹预测基准。
@misc{hoque2025egodex,
title = {{EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video}},
author = {Ryan Hoque and Peide Huang and David J. Yoon and Mouli Sivapurapu and Jian Zhang},
year = {2025},
eprint = {2505.11709},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2505.11709},
}
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},
}
One model that decides when to think and when to act: it reasons at key moments (subtask done, error, human input) and otherwise acts on its latest reasoning; co-trained with synthetic reasoning-rich VL data.单一模型自主决定何时“想”、何时“做”：在子任务完成、出错或人类插话等关键时刻进行推理，其余时间依据最新推理结果生成动作；并与合成的推理型视觉-语言数据共同训练。
@misc{lin2025onetwovla,
title = {{OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning}},
author = {Fanqi Lin and Ruiqian Nai and Yingdong Hu and Jiacheng You and Junming Zhao and Yang Gao},
year = {2025},
eprint = {2505.11917},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.11917},
}
Online RL for VLAs with GRPO and only binary success rewards on OpenVLA-OFT, plus exploration tweaks; reports 99.1% on LIBERO and 70.4% on RoboTwin 1.0 (vs 39.8% for the SFT baseline).在 OpenVLA-OFT 上用 GRPO 与仅"成功/失败"二值奖励做在线强化学习，并加入若干促进探索的改动；论文报告 LIBERO 成功率 99.1%、RoboTwin 1.0 为 70.4%（SFT 基线 39.8%）。
@misc{li2025simplevla,
title = {{SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning}},
author = {Haozhan Li and Yuxin Zuo and Jiale Yu and Yuhao Zhang and Zhaohui Yang and Kaiyan Zhang and Xuekai Zhu and Yuchen Zhang and Tianxing Chen and Ganqu Cui and Dehui Wang and Dingxiang Luo and Yuchen Fan and Youbang Sun and Jia Zeng and Jiangmiao Pang and Shanghang Zhang and Yu Wang and Yao Mu and Bowen Zhou and Ning Ding},
year = {2025},
eprint = {2509.09674},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2509.09674},
}
Builds a bimanual VLA by composing two copies of a pretrained single-arm VLA instead of pretraining on bimanual data; reports beating a similar-size RDT-1B and narrowing the gap to π0 on real and sim tasks.不做双臂预训练，而是把两份预训练单臂 VLA 组合成协同的双臂 VLA；论文报告在真实与仿真双臂任务上优于同规模 RDT-1B，并缩小了与依赖大量双臂数据的 π0 的差距。
@misc{im2025twinvla,
title = {{TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models}},
author = {Hokyun Im and Euijin Jeong and Andrey Kolobov and Jianlong Fu and Youngwoon Lee},
year = {2025},
eprint = {2511.05275},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2511.05275},
}
Gives each data source its own small set of learnable soft-prompt embeddings, so one plain-Transformer flow-matching policy (0.9B) trains across embodiments; evaluated on 6 sim benchmarks and 3 real robots.为每个数据来源配一组可学习的软提示嵌入，使一个基于标准 Transformer 的流匹配策略（0.9B）能跨本体联合训练；在 6 个仿真基准和 3 台真实机器人上评测。
@misc{zheng2025x,
title = {{X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model}},
author = {Jinliang Zheng and Jianxiong Li and Zhihao Wang and Dongxiu Liu and Xirui Kang and Yuchun Feng and Yinan Zheng and Jiayin Zou and Yilun Chen and Jia Zeng and Ya-Qin Zhang and Jiangmiao Pang and Jingjing Liu and Tai Wang and Xianyuan Zhan},
year = {2025},
eprint = {2510.10274},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2510.10274},
}