5 embodied-AI and robotics papers accepted at CoRL 2023, 3 with code.5 篇被 CoRL 2023 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。
RotateIt trains in simulation with privileged object information, then distills to a policy that rotates diverse objects in hand using vision, touch and proprioception on a real hand.RotateIt 先在仿真中利用特权物体信息训练，再蒸馏为仅用视觉、触觉和本体感觉的策略，在真实灵巧手上旋转多种物体。
@misc{qi2023general,
title = {{General In-Hand Object Rotation with Vision and Touch}},
author = {Haozhi Qi and Brent Yi and Sudharshan Suresh and Mike Lambeta and Yi Ma and Roberto Calandra and Jitendra Malik},
year = {2023},
eprint = {2309.09979},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.09979},
}
Turns a handful of human demos into thousands by splitting them into object-centric segments and replaying those segments in new scenes; widely reused to scale imitation data.把少量人类演示切分为以物体为中心的片段，并在新场景中变换重放，从而扩增为数千条演示；被广泛用于扩充模仿学习数据。
@misc{mandlekar2023mimicgen,
title = {{MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations}},
author = {Ajay Mandlekar and Soroush Nasiriany and Bowen Wen and Iretiayo Akinola and Yashraj Narang and Linxi Fan and Yuke Zhu and Dieter Fox},
year = {2023},
eprint = {2310.17596},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.17596},
}
Discretizes each action dimension and predicts Q-values per dimension autoregressively with a Transformer, plus a conservative regularizer; trained offline on large real robot datasets mixing demos and autonomous data.将每个动作维度离散化，用 Transformer 自回归地逐维预测 Q 值，并加入保守正则；在混合演示与自主采集数据的大规模真实机器人数据集上离线训练。
@misc{chebotar2023q,
title = {{Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions}},
author = {Yevgen Chebotar and Quan Vuong and Alex Irpan and Karol Hausman and Fei Xia and Yao Lu and Aviral Kumar and Tianhe Yu and Alexander Herzog and Karl Pertsch and Keerthana Gopalakrishnan and Julian Ibarz and Ofir Nachum and Sumedh Sontakke and Grecia Salazar and Huong T Tran and Jodilyn Peralta and Clayton Tan and Deeksha Manjunath and Jaspiar Singht and Brianna Zitkovich and Tomas Jackson and Kanishka Rao and Chelsea Finn and Sergey Levine},
year = {2023},
eprint = {2309.10150},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.10150},
}
RVT — re-renders the point cloud into a few virtual views and uses a multi-view Transformer to predict keyframe poses; reported to train and run much faster than voxel-based PerAct on RLBench. Repo also hosts RVT-2.RVT：把点云重新渲染为若干虚拟视角，再用多视角 Transformer 预测关键帧位姿；论文报告在 RLBench 上训练与推理都比基于体素的 PerAct 快得多。代码库同时包含 RVT-2。
@misc{goyal2023rvt,
title = {{RVT: Robotic View Transformer for 3D Object Manipulation}},
author = {Ankit Goyal and Jie Xu and Yijie Guo and Valts Blukis and Yu-Wei Chao and Dieter Fox},
year = {2023},
eprint = {2306.14896},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2306.14896},
}
A Transformer navigation policy trained on many robots' driving data that adapts to new goal types and robots with light fine-tuning.在多种机器人的导航数据上训练的 Transformer 导航策略，经少量微调即可适配新的目标形式和新机器人。
@misc{shah2023vint,
title = {{ViNT: A Foundation Model for Visual Navigation}},
author = {Dhruv Shah and Ajay Sridhar and Nitish Dashora and Kyle Stachowicz and Kevin Black and Noriaki Hirose and Sergey Levine},
year = {2023},
eprint = {2306.14846},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2306.14846},
}