UC Berkeley · embodied AI & robot learning加州大学伯克利分校 · 具身智能与机器人学习
42 curated papers, code, datasets and hardware from UC Berkeley (27 with code), with one-line takeaways.42 个来自加州大学伯克利分校的精选论文、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},
}
HIL-SERL — real-world RL with human corrections, reported to reach near-perfect success on precise assembly and dynamic tasks after a few hours of training.HIL-SERL:结合人类纠正的真机强化学习,报告称在精密装配与动态任务上经数小时训练即可接近满分成功率。
@misc{luo2024precise,
title = {{Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}},
author = {Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
year = {2024},
eprint = {2410.21845},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.21845},
}
@misc{radosavovic2023real,
title = {{Real-World Humanoid Locomotion with Reinforcement Learning}},
author = {Ilija Radosavovic and Tete Xiao and Bike Zhang and Trevor Darrell and Jitendra Malik and Koushil Sreenath},
year = {2023},
eprint = {2303.03381},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.03381},
}
Open-source humanoid with 3D-printed modular gearboxes, buildable for under US$5,000; hardware designs, embedded code and training code are released.开源人形机器人,采用 3D 打印的模块化减速器,在美国可用不到 5000 美元搭建;硬件设计、嵌入式代码与训练框架均已开源。
@misc{chi2025demonstrating,
title = {{Demonstrating Berkeley Humanoid Lite: An Open-source, Accessible, and Customizable 3D-printed Humanoid Robot}},
author = {Yufeng Chi and Qiayuan Liao and Junfeng Long and Xiaoyu Huang and Sophia Shao and Borivoje Nikolic and Zhongyu Li and Koushil Sreenath},
year = {2025},
eprint = {2504.17249},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.17249},
}
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},
}
FQL — a flow-matching BC policy plus a one-step policy that maximizes Q while distilling from it; no backprop through flow steps, strong on 73 OGBench / D4RL offline and offline-to-online tasks.FQL:先用行为克隆训练流匹配策略,再训练一个最大化 Q 值、同时向流策略蒸馏的单步策略;避免对多步流采样反向传播,在 73 个 OGBench / D4RL 离线及离线转在线任务上表现突出。
GPU-accelerated MJX environments for locomotion and manipulation that train in minutes and transfer to real robots.基于 MJX 的 GPU 加速运动与操作环境,几分钟即可训练并迁移到真实机器人。
@misc{zakka2025mujoco,
title = {{MuJoCo Playground}},
author = {Kevin Zakka and Baruch Tabanpour and Qiayuan Liao and Mustafa Haiderbhai and Samuel Holt and Jing Yuan Luo and Arthur Allshire and Erik Frey and Koushil Sreenath and Lueder A. Kahrs and Carmelo Sferrazza and Yuval Tassa and Pieter Abbeel},
year = {2025},
eprint = {2502.08844},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.08844},
}
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},
}
DSRL — adapts a frozen diffusion policy by running RL over its input noise, not its weights; needs only forward passes, works with π0, and reports real-robot success rising from 20% to 90% in under 50 online episodes.DSRL:不改扩散策略的权重,而是对其输入的初始噪声做强化学习来"引导"输出动作;只需前向推理、可用于 π0,论文报告真机任务在不到 50 次在线交互内成功率从 20% 升至 90%。
@misc{wagenmaker2025steering,
title = {{Steering Your Diffusion Policy with Latent Space Reinforcement Learning}},
author = {Andrew Wagenmaker and Mitsuhiko Nakamoto and Yunchu Zhang and Seohong Park and Waleed Yagoub and Anusha Nagabandi and Abhishek Gupta and Sergey Levine},
year = {2025},
eprint = {2506.15799},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.15799},
}
TVL pairs tactile and visual observations with language descriptions and trains a touch-vision-language encoder plus a model that describes how things feel.TVL 数据集将触觉与视觉观测配上语言描述,并训练触觉-视觉-语言编码器以及能描述物体触感的模型。
@misc{fu2024touch,
title = {{A Touch, Vision, and Language Dataset for Multimodal Alignment}},
author = {Letian Fu and Gaurav Datta and Huang Huang and William Chung-Ho Panitch and Jaimyn Drake and Joseph Ortiz and Mustafa Mukadam and Mike Lambeta and Roberto Calandra and Ken Goldberg},
year = {2024},
eprint = {2402.13232},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2402.13232},
}
@misc{liao2024berkeley,
title = {{Berkeley Humanoid: A Research Platform for Learning-based Control}},
author = {Qiayuan Liao and Bike Zhang and Xuanyu Huang and Xiaoyu Huang and Zhongyu Li and Koushil Sreenath},
year = {2024},
eprint = {2407.21781},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.21781},
}
Digit 360: an open-design, finger-shaped sensor that senses force, fine texture, vibration and heat, with an on-board neural accelerator for reflex-like local processing.Digit 360:开放设计的指尖形触觉传感器,可感知力、细微纹理、振动和温度,并用板载神经网络加速器做类似反射弧的本地处理。
@misc{lambeta2024digitizing,
title = {{Digitizing Touch with an Artificial Multimodal Fingertip}},
author = {Mike Lambeta and Tingfan Wu and Ali Sengul and Victoria Rose Most and Nolan Black and Kevin Sawyer and Romeo Mercado and Haozhi Qi and Alexander Sohn and Byron Taylor and Norb Tydingco and Gregg Kammerer and Dave Stroud and Jake Khatha and Kurt Jenkins and Kyle Most and Neal Stein and Ricardo Chavira and Thomas Craven-Bartle and Eric Sanchez and Yitian Ding and Jitendra Malik and Roberto Calandra},
year = {2024},
eprint = {2411.02479},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.02479},
}
@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},
}
27 simulated tasks (12 locomotion, 15 whole-body manipulation) on a Unitree H1 with two dexterous hands; reports that DreamerV3, TD-MPC2, SAC and PPO fail most of them, especially long-horizon whole-body ones.27 个仿真任务(12 个运动、15 个全身操作),机器人为带两只灵巧手的 Unitree H1;报告 DreamerV3、TD-MPC2、SAC、PPO 在大多数任务上未达标,长时程全身协调任务尤甚。
@misc{sferrazza2024humanoidbench,
title = {{HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation}},
author = {Carmelo Sferrazza and Dun-Ming Huang and Xingyu Lin and Youngwoon Lee and Pieter Abbeel},
year = {2024},
eprint = {2403.10506},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.10506},
}
HATO — bimanual setup from two UR5e arms and repurposed prosthetic hands with fingertip touch sensors, VR teleop for data collection, and visuotactile policies for tasks like pouring and handovers.HATO:用两台 UR5e 机械臂和改装的假肢手(指尖带触觉传感器)搭建双臂系统,用 VR 遥操作采集数据,并训练视觉-触觉策略完成倒酒、交接等任务。
@misc{lin2024learning,
title = {{Learning Visuotactile Skills with Two Multifingered Hands}},
author = {Toru Lin and Yu Zhang and Qiyang Li and Haozhi Qi and Brent Yi and Sergey Levine and Jitendra Malik},
year = {2024},
eprint = {2404.16823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2404.16823},
}
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},
}
An open-source stack for training manipulation policies with RL on a real arm — learned reward classifiers, reset-free training and an impedance controller.在真实机械臂上用强化学习训练操作策略的开源软件栈:学习式奖励分类器、免重置训练与阻抗控制器。
@misc{luo2024serl,
title = {{SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning}},
author = {Jianlan Luo and Zheyuan Hu and Charles Xu and You Liang Tan and Jacob Berg and Archit Sharma and Stefan Schaal and Chelsea Finn and Abhishek Gupta and Sergey Levine},
year = {2024},
eprint = {2401.16013},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.16013},
}
@misc{walke2023bridgedata,
title = {{BridgeData V2: A Dataset for Robot Learning at Scale}},
author = {Homer Walke and Kevin Black and Abraham Lee and Moo Jin Kim and Max Du and Chongyi Zheng and Tony Zhao and Philippe Hansen-Estruch and Quan Vuong and Andre He and Vivek Myers and Kuan Fang and Chelsea Finn and Sergey Levine},
year = {2023},
eprint = {2308.12952},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2308.12952},
}
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},
}
RLPD — a few simple changes to off-policy RL (symmetric sampling of offline and online data, layer norm, ensembles) make prior data speed up online learning.RLPD:对离策略强化学习做几处简单改动(离线/在线数据对称采样、层归一化、集成),让已有数据加速在线学习。
@misc{ball2023efficient,
title = {{Efficient Online Reinforcement Learning with Offline Data}},
author = {Philip J. Ball and Laura Smith and Ilya Kostrikov and Sergey Levine},
year = {2023},
eprint = {2302.02948},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2302.02948},
}
@misc{wu2023gello,
title = {{GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators}},
author = {Philipp Wu and Yide Shentu and Zhongke Yi and Xingyu Lin and Pieter Abbeel},
year = {2023},
eprint = {2309.13037},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.13037},
}
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},
}
UniSim — an action-conditioned video model used as a simulator to train policies that transfer to the real world.UniSim:以动作为条件的视频生成模型充当仿真器,训练出的策略可迁移到真实世界。
@misc{yang2023learning,
title = {{Learning Interactive Real-World Simulators}},
author = {Sherry Yang and Yilun Du and Kamyar Ghasemipour and Jonathan Tompson and Leslie Kaelbling and Dale Schuurmans and Pieter Abbeel},
year = {2023},
eprint = {2310.06114},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2310.06114},
}
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},
}
@misc{wu2022daydreamer,
title = {{DayDreamer: World Models for Physical Robot Learning}},
author = {Philipp Wu and Alejandro Escontrela and Danijar Hafner and Ken Goldberg and Pieter Abbeel},
year = {2022},
eprint = {2206.14176},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2206.14176},
}
World Models世界模型Quadruped / legged四足 / 足式realquadrupedarmmodel-based-rl
The predecessor of ViNT — one goal-conditioned navigation policy trained on pooled data from several different robots, which transfers to new robots including ones not seen in training.ViNT 的前作:在多种不同机器人汇集的数据上训练一个目标条件导航策略,可迁移到包括训练中未见过的新机器人。
@misc{shah2022gnm,
title = {{GNM: A General Navigation Model to Drive Any Robot}},
author = {Dhruv Shah and Ajay Sridhar and Arjun Bhorkar and Noriaki Hirose and Sergey Levine},
year = {2022},
eprint = {2210.03370},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2210.03370},
}
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},
}
Diffuser — treats planning as denoising whole trajectories, so goals and rewards can steer sampling; an early link between diffusion and control.Diffuser:把规划视为对整条轨迹去噪,目标与奖励可以引导采样;扩散模型与控制结合的早期代表作。
@misc{janner2022planning,
title = {{Planning with Diffusion for Flexible Behavior Synthesis}},
author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},
year = {2022},
eprint = {2205.09991},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2205.09991},
}
A discriminator trained on unstructured mocap clips acts as a style reward, so RL learns tasks with natural motion without hand-picked reference clips; widely reused as a gait prior for legged robots.用在无结构动捕片段上训练的判别器作为"风格奖励",强化学习无需逐段指定参考动作即可学到自然的动作;后来被广泛用作足式机器人的步态先验。
@misc{peng2021amp,
title = {{AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control}},
author = {Xue Bin Peng and Ze Ma and Pieter Abbeel and Sergey Levine and Angjoo Kanazawa},
year = {2021},
eprint = {2104.02180},
archivePrefix = {arXiv},
primaryClass = {cs.GR},
url = {https://arxiv.org/abs/2104.02180},
}
Recasts RL as sequence modeling — a GPT-style model predicts actions conditioned on the desired return; an early bridge between transformers and control.把强化学习改写成序列建模:GPT 式模型以期望回报为条件预测动作;是 Transformer 与控制结合的早期代表。
@misc{chen2021decision,
title = {{Decision Transformer: Reinforcement Learning via Sequence Modeling}},
author = {Lili Chen and Kevin Lu and Aravind Rajeswaran and Kimin Lee and Aditya Grover and Michael Laskin and Pieter Abbeel and Aravind Srinivas and Igor Mordatch},
year = {2021},
eprint = {2106.01345},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2106.01345},
}
IQL — learns values with expectile regression without ever querying actions outside the dataset, then extracts a policy by advantage-weighted regression; a common base for offline-to-online robot RL.IQL:用期望分位数回归学习价值函数、从不查询数据集外的动作,再用优势加权回归提取策略;是机器人离线到在线强化学习的常用基础。
@misc{kumar2021rma,
title = {{RMA: Rapid Motor Adaptation for Legged Robots}},
author = {Ashish Kumar and Zipeng Fu and Deepak Pathak and Jitendra Malik},
year = {2021},
eprint = {2107.04034},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2107.04034},
}
Advantage-weighted actor-critic that pre-trains on prior demonstrations and keeps improving online, shown on real dexterous-hand tasks.优势加权的 Actor-Critic:先用已有示范数据预训练,再在线持续提升,并在真实灵巧手任务上验证。
CQL — penalizes Q-values on actions outside the dataset so the learned value is a lower bound; a standard offline-RL baseline for learning from fixed robot logs.CQL:压低数据集外动作的 Q 值,使学到的价值成为下界;是从固定机器人日志中学习的标准离线强化学习基线。
@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},
}
Off-policy RL with an entropy bonus; sample-efficient enough to become the default for learning directly on real robots.带熵奖励的离策略强化学习;样本效率较高,成为真机上直接学习的常用默认算法。
@misc{haarnoja2018soft,
title = {{Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor}},
author = {Tuomas Haarnoja and Aurick Zhou and Pieter Abbeel and Sergey Levine},
year = {2018},
eprint = {1801.01290},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1801.01290},
}
Short tutorial on Hamilton-Jacobi reachability — computing the set of states from which a system can be kept safe under worst-case disturbances, and the tricks used to scale it up.Hamilton-Jacobi 可达性分析简明教程:计算在最坏扰动下仍能保持系统安全的状态集合,以及将其扩展到更高维系统的方法。
@misc{bansal2017hamilton,
title = {{Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances}},
author = {Somil Bansal and Mo Chen and Sylvia Herbert and Claire J. Tomlin},
year = {2017},
eprint = {1709.07523},
archivePrefix = {arXiv},
primaryClass = {eess.SY},
url = {https://arxiv.org/abs/1709.07523},
}
@misc{peng2017sim,
title = {{Sim-to-Real Transfer of Robotic Control with Dynamics Randomization}},
author = {Xue Bin Peng and Marcin Andrychowicz and Wojciech Zaremba and Pieter Abbeel},
year = {2017},
eprint = {1710.06537},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1710.06537},
}
The YCB object set — 73 everyday objects with scans, textured meshes and simulation models, plus benchmarking protocols; widely used in grasping and pose-estimation work.YCB 物体集:73 个日常物体,附扫描数据、带纹理网格与仿真模型,以及基准测试规程;被抓取与位姿估计研究广泛使用。
@misc{calli2015benchmarking,
title = {{Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols}},
author = {Berk Calli and Aaron Walsman and Arjun Singh and Siddhartha Srinivasa and Pieter Abbeel and Aaron M. Dollar},
year = {2015},
eprint = {1502.03143},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1502.03143},
}