Stanford · embodied AI & robot learning斯坦福大学 · 具身智能与机器人学习
35 curated papers, code, datasets and hardware from Stanford (27 with code), with one-line takeaways.35 个来自斯坦福大学的精选论文、代码、数据集与硬件(27 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。
One motion-tracking recipe with shared hyperparameters learns agile skills (cartwheels, flip-kicks); a guided latent diffusion model composes them for new goals like joystick control, zero-shot on hardware.用同一套超参数的运动跟踪方案学会侧手翻、飞踢、冲刺等敏捷技能,再用带引导的潜空间扩散模型组合这些技能以完成摇杆遥控、避障等新目标,零样本迁移到真机。
@misc{liao2025beyondmimic,
title = {{BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion}},
author = {Qiayuan Liao and Takara E. Truong and Xiaoyu Huang and Yuman Gao and Guy Tevet and Koushil Sreenath and C. Karen Liu},
year = {2025},
eprint = {2508.08241},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.08241},
}
A 7B open VLA built on Llama 2 + DINOv2/SigLIP; the de-facto open baseline for VLA research and LoRA fine-tuning.基于 Llama 2 与 DINOv2/SigLIP 的 7B 开源 VLA,是 VLA 研究与 LoRA 微调的事实开源基线。
@misc{kim2024openvla,
title = {{OpenVLA: An Open-Source Vision-Language-Action Model}},
author = {Moo Jin Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
year = {2024},
eprint = {2406.09246},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.09246},
}
@misc{chi2024universal,
title = {{Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots}},
author = {Cheng Chi and Zhenjia Xu and Chuer Pan and Eric Cousineau and Benjamin Burchfiel and Siyuan Feng and Russ Tedrake and Shuran Song},
year = {2024},
eprint = {2402.10329},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.10329},
}
@misc{zhao2023learning,
title = {{Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware}},
author = {Tony Z. Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
year = {2023},
eprint = {2304.13705},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2304.13705},
}
A whole-body teleoperation interface plus a learning method for a wheeled bimanual robot doing long household tasks that need reach, balance and navigation.面向轮式双臂机器人的全身遥操作接口与学习方法,用于需要伸展、平衡与导航的长程家务任务。
@misc{jiang2025behavior,
title = {{BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities}},
author = {Yunfan Jiang and Ruohan Zhang and Josiah Wong and Chen Wang and Yanjie Ze and Hang Yin and Cem Gokmen and Shuran Song and Jiajun Wu and Li Fei-Fei},
year = {2025},
eprint = {2503.05652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.05652},
}
DexUMI — an exoskeleton matched to the robot hand's fingertip workspace, plus inpainting that swaps the human hand for the robot's; reports 86% average success (Inspire, XHand) and ~3.2× faster collection than teleop.DexUMI:按目标机器人手的指尖工作空间设计的可穿戴手部外骨骼,再用视频修补把画面中的人手换成机器人手;论文报告在 Inspire 与 XHand 上平均成功率 86%,采集效率约为遥操作的 3.2 倍。
@misc{xu2025dexumi,
title = {{DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation}},
author = {Mengda Xu and Han Zhang and Yifan Hou and Zhenjia Xu and Linxi Fan and Manuela Veloso and Shuran Song},
year = {2025},
eprint = {2505.21864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.21864},
}
Compresses action chunks with a DCT-based tokenizer so autoregressive VLAs can learn high-frequency, dexterous control.用基于 DCT 的分词器压缩动作块,使自回归 VLA 能学习高频灵巧控制。
@misc{pertsch2025fast,
title = {{FAST: Efficient Action Tokenization for Vision-Language-Action Models}},
author = {Karl Pertsch and Kyle Stachowicz and Brian Ichter and Danny Driess and Suraj Nair and Quan Vuong and Oier Mees and Chelsea Finn and Sergey Levine},
year = {2025},
eprint = {2501.09747},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2501.09747},
}
OpenVLA-OFT — parallel decoding, action chunking and continuous actions make VLA fine-tuning much faster and more accurate.OpenVLA-OFT:并行解码、动作分块与连续动作让 VLA 微调更快、成功率更高。
@misc{kim2025fine,
title = {{Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success}},
author = {Moo Jin Kim and Chelsea Finn and Percy Liang},
year = {2025},
eprint = {2502.19645},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.19645},
}
MetaSim puts many simulators (Isaac Sim/Gym, MuJoCo, Genesis, SAPIEN…) behind one interface; adds a large synthetic dataset migrated from public benchmarks and unified IL/RL benchmarks.MetaSim 用统一接口封装多种仿真器(Isaac Sim/Gym、MuJoCo、Genesis、SAPIEN、PyBullet 等);提供由公开基准迁移而来的大规模合成数据集,以及按泛化层级划分的统一模仿学习/强化学习基准。
@misc{geng2025roboverse,
title = {{RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning}},
author = {Haoran Geng and Feishi Wang and Songlin Wei and Yuyang Li and Bangjun Wang and Boshi An and Charlie Tianyue Cheng and Haozhe Lou and Peihao Li and Yen-Jen Wang and Yutong Liang and Dylan Goetting and Chaoyi Xu and Haozhe Chen and Yuxi Qian and Yiran Geng and Jiageng Mao and Weikang Wan and Mingtong Zhang and Jiangran Lyu and Siheng Zhao and Jiazhao Zhang and Jialiang Zhang and Chengyang Zhao and Haoran Lu and Yufei Ding and Ran Gong and Yuran Wang and Yuxuan Kuang and Ruihai Wu and Baoxiong Jia and Carlo Sferrazza and Hao Dong and Siyuan Huang and Yue Wang and Jitendra Malik and Pieter Abbeel},
year = {2025},
eprint = {2504.18904},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.18904},
}
A small, 3D-printed open-source humanoid with a matching simulation model, built so that ML policies and teleoperated data transfer to the real robot.3D 打印的小型开源人形机器人,配套仿真模型,便于机器学习策略与遥操作数据迁移到真机。
@misc{shi2025toddlerbot,
title = {{ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation}},
author = {Haochen Shi and Weizhuo Wang and Shuran Song and C. Karen Liu},
year = {2025},
eprint = {2502.00893},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.00893},
}
UVA — one shared latent for video and actions with two light diffusion heads; masked training lets the same model act as policy, video generator and forward/inverse dynamics model, while keeping policy inference fast.UVA:视频与动作共享一个潜表示,配两个轻量扩散头解码;通过掩码训练,同一模型既能当策略,也能做视频生成和正/逆动力学,且策略推理仍然较快。
@misc{wang2024dexcap,
title = {{DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation}},
author = {Chen Wang and Haochen Shi and Weizhuo Wang and Ruohan Zhang and Li Fei-Fei and C. Karen Liu},
year = {2024},
eprint = {2403.07788},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07788},
}
@misc{li2024evaluating,
title = {{Evaluating Real-World Robot Manipulation Policies in Simulation}},
author = {Xuanlin Li and Kyle Hsu and Jiayuan Gu and Karl Pertsch and Oier Mees and Homer Rich Walke and Chuyuan Fu and Ishikaa Lunawat and Isabel Sieh and Sean Kirmani and Sergey Levine and Jiajun Wu and Chelsea Finn and Hao Su and Quan Vuong and Ted Xiao},
year = {2024},
eprint = {2405.05941},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.05941},
}
@misc{fu2024humanplus,
title = {{HumanPlus: Humanoid Shadowing and Imitation from Humans}},
author = {Zipeng Fu and Qingqing Zhao and Qi Wu and Gordon Wetzstein and Chelsea Finn},
year = {2024},
eprint = {2406.10454},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.10454},
}
Puts ALOHA on a mobile base; co-training with static ALOHA data boosts mobile tasks like cooking and opening cabinets.将 ALOHA 装上移动底盘;与静态 ALOHA 数据联合训练显著提升做饭、开柜等移动任务表现。
@misc{fu2024mobile,
title = {{Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation}},
author = {Zipeng Fu and Tony Z. Zhao and Chelsea Finn},
year = {2024},
eprint = {2401.02117},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.02117},
}
A small open diffusion-head Transformer pretrained on Open X-Embodiment that fine-tunes quickly to new robots and sensors.在 Open X-Embodiment 上预训练的开源扩散头 Transformer 策略,可快速微调到新机器人和新传感器。
@misc{team2024octo,
title = {{Octo: An Open-Source Generalist Robot Policy}},
author = {Octo Model Team and Dibya Ghosh and Homer Walke and Karl Pertsch and Kevin Black and Oier Mees and Sudeep Dasari and Joey Hejna and Tobias Kreiman and Charles Xu and Jianlan Luo and You Liang Tan and Lawrence Yunliang Chen and Pannag Sanketi and Quan Vuong and Ted Xiao and Dorsa Sadigh and Chelsea Finn and Sergey Levine},
year = {2024},
eprint = {2405.12213},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.12213},
}
ReKep — a VLM writes manipulation tasks as Python cost functions over 3D keypoints (relational keypoint constraints), solved by a hierarchical optimizer in real time; zero-shot, no task-specific training.ReKep:由视觉语言模型把操作任务写成定义在三维关键点上的 Python 代价函数(关系关键点约束),再由分层优化器实时求解;零样本,无需针对任务训练。
@misc{huang2024rekep,
title = {{ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation}},
author = {Wenlong Huang and Chen Wang and Yunzhu Li and Ruohan Zhang and Li Fei-Fei},
year = {2024},
eprint = {2409.01652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.01652},
}
One modular teleop interface that mixes RGB-D body tracking, VR controllers, keyboard and joystick for whole-body mobile manipulators (Tiago++, HSR, Fetch), in sim and real.模块化遥操作接口,可组合 RGB-D 人体追踪、VR 手柄、键盘与摇杆,控制 Tiago++、HSR、Fetch 等移动操作机器人做全身动作,仿真和真机都支持。
@misc{dass2024telemoma,
title = {{TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation}},
author = {Shivin Dass and Wensi Ai and Yuqian Jiang and Samik Singh and Jiaheng Hu and Ruohan Zhang and Peter Stone and Ben Abbatematteo and Roberto Martín-Martín},
year = {2024},
eprint = {2403.07869},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07869},
}
An inexpensive open-source mobile base with powered casters, so it is fully holonomic; carries arbitrary arms and comes with phone teleoperation for collecting mobile-manipulation demos for imitation learning.低成本开源移动底盘,采用主动脚轮实现全向(完整约束)运动,可搭载任意机械臂,并配有手机遥操作界面,方便为模仿学习采集移动操作示教数据。
@misc{wu2024tidybot,
title = {{TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning}},
author = {Jimmy Wu and William Chong and Robert Holmberg and Aaditya Prasad and Yihuai Gao and Oussama Khatib and Shuran Song and Szymon Rusinkiewicz and Jeannette Bohg},
year = {2024},
eprint = {2412.10447},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.10447},
}
Keeps conservative offline Q-values from dropping below a reference policy's value, which avoids the early performance dip when an offline-pretrained policy starts online fine-tuning.约束保守的离线 Q 值不低于参考策略的价值,从而避免离线预训练策略开始在线微调时常见的性能先降后升。
@misc{nakamoto2023cal,
title = {{Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning}},
author = {Mitsuhiko Nakamoto and Yuexiang Zhai and Anikait Singh and Max Sobol Mark and Yi Ma and Chelsea Finn and Aviral Kumar and Sergey Levine},
year = {2023},
eprint = {2303.05479},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2303.05479},
}
Survey of how pretrained foundation models are used in robot perception, decision-making and control, and what holds them back — scarce robot data, safety and uncertainty, real-time execution.综述预训练基础模型如何用于机器人的感知、决策与控制,以及主要瓶颈:机器人数据稀缺、安全性与不确定性量化、实时执行。
@misc{firoozi2023foundation,
title = {{Foundation Models in Robotics: Applications, Challenges, and the Future}},
author = {Roya Firoozi and Johnathan Tucker and Stephen Tian and Anirudha Majumdar and Jiankai Sun and Weiyu Liu and Yuke Zhu and Shuran Song and Ashish Kapoor and Karol Hausman and Brian Ichter and Danny Driess and Jiajun Wu and Cewu Lu and Mac Schwager},
year = {2023},
eprint = {2312.07843},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.07843},
}
Distills specialist parkour skills into a single vision-based policy that climbs, leaps and crawls on low-cost quadrupeds.将多个跑酷专家技能蒸馏为单一视觉策略,让低成本四足机器人攀爬、跳跃和匍匐。
@misc{zhuang2023robot,
title = {{Robot Parkour Learning}},
author = {Ziwen Zhuang and Zipeng Fu and Jianren Wang and Christopher Atkeson and Soeren Schwertfeger and Chelsea Finn and Hang Zhao},
year = {2023},
eprint = {2309.05665},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.05665},
}
LLMs write code that composes 3D affordance/constraint maps, which a planner turns into trajectories — no robot training.大模型编写代码组合 3D 可供性/约束地图,再由规划器生成轨迹,无需机器人训练。
@misc{huang2023voxposer,
title = {{VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models}},
author = {Wenlong Huang and Chen Wang and Ruohan Zhang and Yunzhu Li and Jiajun Wu and Li Fei-Fei},
year = {2023},
eprint = {2307.05973},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.05973},
}
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},
}
@misc{nair2022r,
title = {{R3M: A Universal Visual Representation for Robot Manipulation}},
author = {Suraj Nair and Aravind Rajeswaran and Vikash Kumar and Chelsea Finn and Abhinav Gupta},
year = {2022},
eprint = {2203.12601},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2203.12601},
}
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},
}
Casts many tabletop tasks as interleaved text-and-image prompts to one Transformer policy, with the VIMA-Bench suite for systematic generalization tests.把多种桌面任务统一成图文交错的提示,交给一个 Transformer 策略执行,并配套 VIMA-Bench 用于系统性泛化评测。
@misc{jiang2022vima,
title = {{VIMA: General Robot Manipulation with Multimodal Prompts}},
author = {Yunfan Jiang and Agrim Gupta and Zichen Zhang and Guanzhi Wang and Yongqiang Dou and Yanjun Chen and Li Fei-Fei and Anima Anandkumar and Yuke Zhu and Linxi Fan},
year = {2022},
eprint = {2210.03094},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2210.03094},
}
robomimic — a controlled study of imitation and offline RL on human-collected manipulation demos (demo quality, observation choices, history-aware BC), released with the framework and datasets.robomimic:在人类采集的操作示教数据上系统比较模仿学习与离线强化学习(示教质量、观测选择、带历史的行为克隆),并开源框架与数据集。
@misc{mandlekar2021what,
title = {{What Matters in Learning from Offline Human Demonstrations for Robot Manipulation}},
author = {Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese and Yuke Zhu and Roberto Martín-Martín},
year = {2021},
eprint = {2108.03298},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2108.03298},
}
A fast, robust ADMM-based QP solver with warm starting and embedded C code generation; widely used inside MPC and whole-body controllers.基于 ADMM 的快速、鲁棒二次规划求解器,支持热启动与嵌入式 C 代码生成,广泛用于 MPC 与全身控制器。
@misc{stellato2017osqp,
title = {{OSQP: An Operator Splitting Solver for Quadratic Programs}},
author = {Bartolomeo Stellato and Goran Banjac and Paul Goulart and Alberto Bemporad and Stephen Boyd},
year = {2017},
eprint = {1711.08013},
archivePrefix = {arXiv},
primaryClass = {math.OC},
url = {https://arxiv.org/abs/1711.08013},
}
@misc{zhu2020robosuite,
title = {{robosuite: A Modular Simulation Framework and Benchmark for Robot Learning}},
author = {Yuke Zhu and Josiah Wong and Ajay Mandlekar and Roberto Martín-Martín and Abhishek Joshi and Kevin Lin and Abhiram Maddukuri and Soroush Nasiriany and Yifeng Zhu},
year = {2020},
eprint = {2009.12293},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2009.12293},
}
@misc{yu2019meta,
title = {{Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning}},
author = {Tianhe Yu and Deirdre Quillen and Zhanpeng He and Ryan Julian and Avnish Narayan and Hayden Shively and Adithya Bellathur and Karol Hausman and Chelsea Finn and Sergey Levine},
year = {2019},
eprint = {1910.10897},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1910.10897},
}
An early shared dataset of robot interaction videos from seven robot platforms, used to pretrain visual dynamics models that transfer to new robots - a forerunner of Open X-Embodiment.早期的共享机器人交互视频数据集,涵盖七种机器人平台,用于预训练可迁移到新机器人的视觉动力学模型,是 Open X-Embodiment 的先驱。
@misc{dasari2019robonet,
title = {{RoboNet: Large-Scale Multi-Robot Learning}},
author = {Sudeep Dasari and Frederik Ebert and Stephen Tian and Suraj Nair and Bernadette Bucher and Karl Schmeckpeper and Siddharth Singh and Sergey Levine and Chelsea Finn},
year = {2019},
eprint = {1910.11215},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1910.11215},
}
Learns a compact vision + force/torque representation with self-supervised objectives, then trains a peg-insertion policy with RL on top of it on a real robot.用自监督目标学习视觉 + 力/力矩的紧凑联合表征,再在其上用强化学习在真实机器人上训练插孔策略。
@misc{lee2018making,
title = {{Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks}},
author = {Michelle A. Lee and Yuke Zhu and Krishnan Srinivasan and Parth Shah and Silvio Savarese and Li Fei-Fei and Animesh Garg and Jeannette Bohg},
year = {2018},
eprint = {1810.10191},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1810.10191},
}
RGB-D scans of 1,500+ indoor scenes with 3D reconstructions, camera poses and semantic labels; the standard benchmark for 3D scene understanding that embodied perception builds on.1500+ 个室内场景的 RGB-D 扫描,含三维重建、相机位姿与语义标注;具身感知所依赖的三维场景理解标准基准。
@misc{dai2017scannet,
title = {{ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes}},
author = {Angela Dai and Angel X. Chang and Manolis Savva and Maciej Halber and Thomas Funkhouser and Matthias Nießner},
year = {2017},
eprint = {1702.04405},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1702.04405},
}
GAIL — learns a policy whose state-action distribution a discriminator cannot tell apart from the expert's, skipping explicit reward recovery.GAIL:训练策略使判别器无法区分其状态-动作分布与专家数据,无需显式恢复奖励函数。