5 embodied-AI and robotics papers accepted at CoRL 2022, 5 with code.5 篇被 CoRL 2022 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。
1,000 household activities chosen from a survey of what people want robots to do, defined in a logic language and simulated in OmniGibson with realistic physics and rendering.依据人们希望机器人做什么的调查挑选出 1000 项家务活动，用逻辑语言定义，并在具备真实物理与渲染的 OmniGibson 中仿真。
@misc{li2024behavior,
title = {{BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation}},
author = {Chengshu Li and Ruohan Zhang and Josiah Wong and Cem Gokmen and Sanjana Srivastava and Roberto Martín-Martín and Chen Wang and Gabrael Levine and Wensi Ai and Benjamin Martinez and Hang Yin and Michael Lingelbach and Minjune Hwang and Ayano Hiranaka and Sujay Garlanka and Arman Aydin and Sharon Lee and Jiankai Sun and Mona Anvari and Manasi Sharma and Dhruva Bansal and Samuel Hunter and Kyu-Young Kim and Alan Lou and Caleb R Matthews and Ivan Villa-Renteria and Jerry Huayang Tang and Claire Tang and Fei Xia and Yunzhu Li and Silvio Savarese and Hyowon Gweon and C. Karen Liu and Jiajun Wu and Li Fei-Fei},
year = {2024},
eprint = {2403.09227},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.09227},
}
Chains an LLM (parses landmarks from the instruction), CLIP (finds them in images) and a visual navigation model to follow route instructions on a real robot without language-annotated robot data.将大语言模型（从指令中解析地标）、CLIP（在图像中找到地标）与视觉导航模型串联，无需带语言标注的机器人数据即可在真实机器人上按路线指令导航。
@misc{shah2022lm,
title = {{LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action}},
author = {Dhruv Shah and Blazej Osinski and Brian Ichter and Sergey Levine},
year = {2022},
eprint = {2207.04429},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2207.04429},
}
PerAct — a Perceiver Transformer over voxelized RGB-D observations that predicts the next keyframe gripper pose as voxel classification; one language-conditioned policy for many RLBench and real tasks.PerAct：在体素化的 RGB-D 观测上运行 Perceiver Transformer，把下一关键帧夹爪位姿当作体素分类来预测；一个语言条件策略即可覆盖众多 RLBench 与真机任务。
Fuses camera, contact microphone and GelSight touch with self-attention in an imitation-learned policy, studied on dense packing and pouring tasks.在模仿学习策略中用自注意力融合相机、接触麦克风和 GelSight 触觉，在密集装箱与倾倒任务上做了研究。
@misc{li2022see,
title = {{See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation}},
author = {Hao Li and Yizhi Zhang and Junzhe Zhu and Shaoxiong Wang and Michelle A Lee and Huazhe Xu and Edward Adelson and Li Fei-Fei and Ruohan Gao and Jiajun Wu},
year = {2022},
eprint = {2212.03858},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2212.03858},
}
Trains one quadruped policy that exposes gait parameters (frequency, foot swing, posture) as commands, so a human can tune behavior at deploy time for new tasks; open sim-to-real stack for the Unitree Go1.训练一个把步态参数（频率、抬脚高度、姿态等）作为指令开放的四足策略，部署时可由人调节行为以适应新任务；附带面向 Unitree Go1 的开源仿真到真实工具链。
@misc{margolis2022walk,
title = {{Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior}},
author = {Gabriel B Margolis and Pulkit Agrawal},
year = {2022},
eprint = {2212.03238},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2212.03238},
}