University of Toronto · embodied AI & robot learning多伦多大学 · 具身智能与机器人学习
6 curated papers, code, datasets and hardware from University of Toronto (3 with code), with one-line takeaways.6 个来自多伦多大学的精选论文、代码、数据集与硬件（3 个附代码），均配一句话要点。
Labs are matched from each entry's affiliation field by simple name rules (data/labs.yaml); joint work appears under every lab named. Only curated entries are listed, because daily arXiv papers carry no affiliations.机构由每个条目的机构字段按名称规则匹配（data/labs.yaml），合作工作会出现在所有署名机构下。仅列出精选条目，因为每日 arXiv 论文不含机构信息。
Builds an object-level 3D scene graph labelled by vision-language models, which an LLM planner can query in natural language.用视觉语言模型构建带语义标注的物体级三维场景图，大模型规划器可以用自然语言查询。
@misc{gu2023conceptgraphs,
title = {{ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning}},
author = {Qiao Gu and Alihusein Kuwajerwala and Sacha Morin and Krishna Murthy Jatavallabhula and Bipasha Sen and Aditya Agarwal and Corban Rivera and William Paul and Kirsty Ellis and Rama Chellappa and Chuang Gan and Celso Miguel de Melo and Joshua B. Tenenbaum and Antonio Torralba and Florian Shkurti and Liam Paull},
year = {2023},
eprint = {2309.16650},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.16650},
}
@misc{mittal2023orbit,
title = {{Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments}},
author = {Mayank Mittal and Calvin Yu and Qinxi Yu and Jingzhou Liu and Nikita Rudin and David Hoeller and Jia Lin Yuan and Ritvik Singh and Yunrong Guo and Hammad Mazhar and Ajay Mandlekar and Buck Babich and Gavriel State and Marco Hutter and Animesh Garg},
year = {2023},
eprint = {2301.04195},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2301.04195},
}
Survey connecting control-theory and RL views of safety, from learned dynamics with guarantees to constrained RL and safety filters.综述，连接控制理论与强化学习两种安全视角：从带保证的学习动力学到约束强化学习与安全滤波器。
@misc{brunke2021safe,
title = {{Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning}},
author = {Lukas Brunke and Melissa Greeff and Adam W. Hall and Zhaocong Yuan and Siqi Zhou and Jacopo Panerati and Angela P. Schoellig},
year = {2021},
eprint = {2108.06266},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2108.06266},
}
Gym-style benchmark with cart-pole and quadrotor tasks, constraints and disturbances for comparing safe control and safe RL methods.Gym 风格基准，含倒立摆与四旋翼任务、约束与扰动，用于比较安全控制与安全强化学习方法。
@misc{yuan2021safe,
title = {{safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics}},
author = {Zhaocong Yuan and Adam W. Hall and Siqi Zhou and Lukas Brunke and Melissa Greeff and Jacopo Panerati and Angela P. Schoellig},
year = {2021},
eprint = {2109.06325},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.06325},
}
EPIC-KITCHENS-100 — 100 hours (20M frames, ~90K action segments) of unscripted first-person kitchen video from 45 environments, with dense action labels and challenges such as action anticipation.EPIC-KITCHENS-100：来自 45 个厨房环境、100 小时（2000 万帧、约 9 万个动作片段）的非脚本第一视角视频，动作标注密集，并设有动作预测等挑战任务。
@misc{damen2020rescaling,
title = {{Rescaling Egocentric Vision}},
author = {Dima Damen and Hazel Doughty and Giovanni Maria Farinella and Antonino Furnari and Evangelos Kazakos and Jian Ma and Davide Moltisanti and Jonathan Munro and Toby Perrett and Will Price and Michael Wray},
year = {2020},
eprint = {2006.13256},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2006.13256},
}
The first Dreamer — learns a latent world model from pixels and trains the policy entirely on imagined rollouts.第一代 Dreamer：从像素学习潜空间世界模型，并完全在想象的轨迹上训练策略。
@misc{hafner2019dream,
title = {{Dream to Control: Learning Behaviors by Latent Imagination}},
author = {Danijar Hafner and Timothy Lillicrap and Jimmy Ba and Mohammad Norouzi},
year = {2019},
eprint = {1912.01603},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1912.01603},
}