University of Washington · embodied AI & robot learning华盛顿大学 · 具身智能与机器人学习
13 curated papers, code, datasets and hardware from University of Washington (10 with code), with one-line takeaways.13 个来自华盛顿大学的精选论文、代码、数据集与硬件（10 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。
Prompt-only task-progress scoring: the VLM first describes what happens and defines absolute success, then scores with an explicit rubric. This curbs hallucinated progress on failed runs, and the rewards can drive RL.只靠提示词的任务进度评估：先让 VLM 客观描述画面并定义绝对成功标准，再按明确评分表打分，减少对失败执行“幻觉式”的进度判断，其奖励可用于下游强化学习。
@misc{memmel2026d,
title = {{D\&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models}},
author = {Marius Memmel and Shawn Yin and Urvi Rutia and Angad Wadhwa and Eldon Li and Gavin Angeloff and Ankit Goyal and Jesse Zhang and Dieter Fox and Fabio Ramos and Anqi Li and Abhishek Gupta},
year = {2026},
eprint = {2611.03622},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03622},
}
Inverse RL from observation only (videos, no actions or rewards) made practical on a real robot via online world modeling. Reports pick-and-place learned from scratch to 82% in under 40 minutes.仅从观测（视频，无动作和奖励）做逆强化学习，借助在线世界模型在真机上实现。报告不到 40 分钟从零学会抓放，成功率 82%。
@misc{han2026online,
title = {{Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation}},
author = {Tyler Han and Bat Nemekhbold and Siyang Shen and Rohan Baijal and Richard Ebock and Harine Ravichandiran and Sanghun Jung and Kevin Huang and Byron Boots},
year = {2026},
eprint = {2602.24121},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.24121},
}
Real users intervene when progress stalls, often with imperfect fixes; a progress-based human model makes sim benchmarking match them. Q-OIL adds optimistic bonuses on intervention steps; reports 10-50% higher success.真实用户往往在进展停滞时介入，且纠正动作并不完美；据此建立的基于进展的人类模型让仿真评测更贴近真人。Q-OIL 对介入转移加乐观奖励并用解耦评论家稳定训练，报告成功率提升 10-50%。
@misc{teegavarapu2026q,
title = {{Q-OIL: Optimistic Exploration in Learning from Human Interventions}},
author = {Pranav Teegavarapu and Sriyash Poddar and Rico Qi and Cleah Winston and Krisha Khandelwal and Aykut Onyol and Natasha Jaques and Abhishek Gupta},
year = {2026},
eprint = {2611.08576},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08576},
}
Instead of one universal gripper: a fixed robot-side coupling plus cheap tool-side adapters with tactile sensors turn household tools into swappable end effectors; imitation policies cook tofu and flip meat.不追求万能夹爪，而是用固定的机器人侧接口加低成本、带触觉传感器的工具侧适配器，把日常厨具变成可自动更换的末端执行器，兼容多种机器人平台；配套遥操作采集示范，模仿学习策略完成煎豆腐、拌沙拉、翻肉等工具任务。
@misc{zhang2026swapbot,
title = {{SwapBot: Tool-Centric Robot Manipulation via Interchangeable End-Effectors and Tactile Sensing}},
author = {Yunchu Zhang and Ray Song and Junhan Jiao and Liyiming Ke and Yuchen Yang and Ruihan Gao and Emma Romig and Siddhartha Srinivasa and Yiyue Luo},
year = {2026},
eprint = {2610.40646},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.40646},
}
A hierarchical VLA in which a fine-tuned VLM draws a coarse 2D path for the end effector on the image and a low-level 3D policy follows it, so the VLM can be trained on cheap off-domain data such as sim or videos.分层 VLA：微调后的 VLM 在图像上画出末端执行器的粗略二维路径，再由底层三维策略跟随执行；因此 VLM 可以用仿真、视频等低成本的非本域数据训练。
@misc{li2025hamster,
title = {{HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation}},
author = {Yi Li and Yuquan Deng and Jesse Zhang and Joel Jang and Marius Memmel and Raymond Yu and Caelan Reed Garrett and Fabio Ramos and Dieter Fox and Anqi Li and Abhishek Gupta and Ankit Goyal},
year = {2025},
eprint = {2502.05485},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.05485},
}
An open 7B VLA that first outputs depth-aware perception tokens, then an editable 2D trajectory, then actions; reports 70.5% zero-shot on SimplerEnv and 86.6% on LIBERO, with weights, code and data released.开源 7B VLA，先输出深度感知 token，再生成可编辑的二维轨迹，最后输出动作；论文报告 SimplerEnv 零样本 70.5%、LIBERO 86.6%，并开放权重、代码和数据。
@misc{lee2025molmoact,
title = {{MolmoAct: Action Reasoning Models that can Reason in Space}},
author = {Jason Lee and Jiafei Duan and Haoquan Fang and Yuquan Deng and Shuo Liu and Boyang Li and Bohan Fang and Jieyu Zhang and Yi Ru Wang and Sangho Lee and Winson Han and Wilbert Pumacay and Angelica Wu and Rose Hendrix and Karen Farley and Eli VanderBilt and Ali Farhadi and Dieter Fox and Ranjay Krishna},
year = {2025},
eprint = {2508.07917},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.07917},
}
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},
}
LAPA pretrains a VLA without action labels: it learns discrete latent actions between video frames, predicts them from image + text, then maps them to robot actions; reports beating OpenVLA on real tasks.LAPA 无需动作标注即可预训练 VLA：先从视频相邻帧间学习离散隐动作，再训练模型由图像和指令预测隐动作，最后用少量机器人数据映射到真实动作；报告在真机任务上优于 OpenVLA。
@misc{ye2024latent,
title = {{Latent Action Pretraining from Videos}},
author = {Seonghyeon Ye and Joel Jang and Byeongguk Jeon and Sejune Joo and Jianwei Yang and Baolin Peng and Ajay Mandlekar and Reuben Tan and Yu-Wei Chao and Bill Yuchen Lin and Lars Liden and Kimin Lee and Jianfeng Gao and Luke Zettlemoyer and Dieter Fox and Minjoon Seo},
year = {2024},
eprint = {2410.11758},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.11758},
}
@misc{pumacay2024colosseum,
title = {{THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation}},
author = {Wilbert Pumacay and Ishika Singh and Jiafei Duan and Ranjay Krishna and Jesse Thomason and Dieter Fox},
year = {2024},
eprint = {2402.08191},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.08191},
}
A neural policy trained on millions of planner-generated trajectories that moves an arm to goals straight from point clouds.在数百万条规划器生成的轨迹上训练的神经策略，可直接根据点云把机械臂移动到目标位置。
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。
CLIPort — combines CLIP's semantic "what" stream with Transporter's spatial "where" stream for language-conditioned tabletop manipulation; an early bridge from web vision-language models to robot actions.CLIPort：把 CLIP 提供的语义"是什么"通路与 Transporter 的空间"在哪里"通路结合，实现语言条件的桌面操作；是较早把网络视觉语言模型接入机器人动作的工作。
@misc{shridhar2021cliport,
title = {{CLIPort: What and Where Pathways for Robotic Manipulation}},
author = {Mohit Shridhar and Lucas Manuelli and Dieter Fox},
year = {2021},
eprint = {2109.12098},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.12098},
}
@misc{chao2021dexycb,
title = {{DexYCB: A Benchmark for Capturing Hand Grasping of Objects}},
author = {Yu-Wei Chao and Wei Yang and Yu Xiang and Pavlo Molchanov and Ankur Handa and Jonathan Tremblay and Yashraj S. Narang and Karl Van Wyk and Umar Iqbal and Stan Birchfield and Jan Kautz and Dieter Fox},
year = {2021},
eprint = {2104.04631},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2104.04631},
}