Demo Data from EDBT 2026 / reviewers in the wild / expert
Yueh-Hua Wu
dblp:211/7212
· DBLP profile ↗
10ranked-venue papers
3as first author
7since 2021 · last 2025
0009-0002-6283-9883ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 10 · 3 first-author · 7 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author · 2 since 2021Systems, architecture and hardware · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
8 papers |
Reinforcement learning · 27% Efficient and distributed learning · 23% Motion planning and robot control · 22% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% |
Topics — the 20 heaviest of 22, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning › model compression
knowledge distillation |
1.7 | 2 | 2025 | Unified Reinforcement and Imitation Learning for Vision-Language Models · NeurIPS 2025 VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models · CVPR 2025 |
Robotics › Motion planning and robot control
robot learning |
1.6 | 2 | 2025 | ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning · NeurIPS 2025 Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Computer vision › Vision and language › vision-language model
efficient vision-language model |
0.9 | 1 | 2025 | VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models · CVPR 2025 |
Robotics › Motion planning and robot control › robot control › learning control
imitation reinforcement learning |
0.9 | 1 | 2025 | Unified Reinforcement and Imitation Learning for Vision-Language Models · NeurIPS 2025 |
Machine learning › Efficient and distributed learning › model compression › knowledge distillation › feature distillation
layer-wise distillation |
0.9 | 1 | 2025 | VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models · CVPR 2025 |
Machine learning › Efficient and distributed learning
model compression |
0.9 | 1 | 2025 | VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models · CVPR 2025 |
Computer vision › Vision and language
vision-language model |
0.9 | 1 | 2025 | VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models · CVPR 2025 |
Computer vision › Vision and language › vision-language model
vision-language model training |
0.9 | 1 | 2025 | Unified Reinforcement and Imitation Learning for Vision-Language Models · NeurIPS 2025 |
Robotics › Motion planning and robot control › robot learning › robot policy learning
generalist robot policy |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Machine learning › Reinforcement learning › offline reinforcement learning
decision transformer |
0.7 | 1 | 2023 | Elastic Decision Transformer · NeurIPS 2023 |
Machine learning › Reinforcement learning
offline reinforcement learning |
0.7 | 1 | 2023 | Elastic Decision Transformer · NeurIPS 2023 |
Machine learning › Reinforcement learning › offline reinforcement learning
trajectory stitching |
0.7 | 1 | 2023 | Elastic Decision Transformer · NeurIPS 2023 |
Robotics › Robot manipulation
dexterous manipulation |
0.6 | 1 | 2022 | DexMV: Imitation Learning for Dexterous Manipulation from Human Videos · ECCV (39) 2022 |
Machine learning › Reinforcement learning
imitation learning |
0.6 | 2 | 2022 | Imitation Learning from Imperfect Demonstration · ICML 2019 DexMV: Imitation Learning for Dexterous Manipulation from Human Videos · ECCV (39) 2022 |
Machine learning › Reinforcement learning › imitation learning › generative imitation learning
generative adversarial imitation learning |
0.4 | 1 | 2019 | Imitation Learning from Imperfect Demonstration · ICML 2019 |
Machine learning › Reinforcement learning › imitation learning
learning from imperfect demonstrations |
0.4 | 1 | 2019 | Imitation Learning from Imperfect Demonstration · ICML 2019 |
Machine learning › Reinforcement learning
safe reinforcement learning |
0.3 | 1 | 2018 | A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents · AAAI 2018 |
Machine learning › Reinforcement learning › reward design
reward shaping |
0.3 | 1 | 2025 | ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning · NeurIPS 2025 |
Machine learning › Reinforcement learning › value-based reinforcement learning
q-learning |
0.2 | 1 | 2023 | Elastic Decision Transformer · NeurIPS 2023 |
Human-robot interaction › robot ethics
value alignment |
0.1 | 1 | 2018 | A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents · AAAI 2018 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 1.7verbalizer · 0.9multimodal LLM · 0.9layer-wise distillation · 0.9latent planning · 0.9adversarial imitation learning · 0.9LLM-based discriminator · 0.9transformer policy · 0.8large-scale pretraining · 0.8decision transformer · 0.7human policy integration · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language ModelsabstractThe recent surge in high-quality visual instruction tuning samples from closed-source vision-language models (VLMs) such as GPT-4V has accelerated the release of open-source VLMs across various model sizes. However, scaling VLMs to improve performance using larger models brings significant computational challenges, especially for deployment on resource-constrained devices like mobile platforms and robots. To address this, we propose VLsI: Verbalized Layers-to-Interactions, a new VLM family in 2B and 7B model sizes, which prioritizes efficiency without compromising accuracy. VLsI leverages a unique, layer-wise distillation process, introducing intermediate "verbalizers" that map features from each layer to natural language space, allowing smaller VLMs to flexibly align with the reasoning processes of larger VLMs. This approach mitigates the training instability often encountered in output imitation and goes beyond typical final-layer tuning by aligning the small VLMs’ layer-wise progression with that of the large ones. We validate VLsI across ten challenging vision-language benchmarks, achieving notable performance gains (11.0% for 2B and 17.4% for 7B) over GPT-4V without the need for model scaling, merging, or architectural changes. Project Page. Ryo Hachiuma, Yu-Chiang Frank Wang, Yong Man Ro, Yueh-Hua Wu |
CVPR | 5 |
| 2025 | DNAct: Diffusion Guided Multi-Task 3D Policy LearningabstractThis paper presents DNAct, a language-conditioned multi-task policy framework that integrates neural rendering pre-training and diffusion training to enforce multi-modality learning in action sequence spaces. To learn a generalizable multi-task policy with few demonstrations, the pre-training phase of DNAct leverages neural rendering to distill 2D semantic features from foundation models such as Stable Diffusion to a 3D space, which provides a comprehensive semantic understanding regarding the scene. Consequently, it allows various applications for challenging robotic tasks requiring rich 3D semantics and accurate geometry. Furthermore, we introduce a novel approach utilizing diffusion training to learn a vision and language feature that encapsulates the inherent multi-modality in the multi-task demonstrations. By reconstructing the action sequences from different tasks via the diffusion process, the model is capable of distinguishing different modalities and thus improving the robustness and the generalizability of the learned representation. DNAct significantly surpasses SOTA NeRF-based multi-task manipulation approaches with over 30% improvement in success rate. Videos are available on dnact.github.io Yueh-Hua Wu |
IROS | 2 |
| 2025 | ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent PlanningabstractVision-language-action (VLA) reasoning tasks require agents to interpret multimodal instructions, perform long-horizon planning, and act adaptively in dynamic environments. Existing approaches typically train VLA models in an end-to-end fashion, directly mapping inputs to actions without explicit reasoning, which hinders their ability to plan over multiple steps or adapt to complex task variations. In this paper, we propose ThinkAct, a dual-system framework that bridges high-level reasoning with low-level action execution via reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate embodied reasoning plans guided by reinforcing action-aligned visual rewards based on goal completion and trajectory consistency. These reasoning plans are compressed into a visual plan latent that conditions a downstream action model for robust action execution on target environments. Extensive experiments on embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct enables few-shot adaptation, long-horizon planning, and self-correction behaviors in complex embodied AI tasks. Chi-Pin Huang, Yueh-Hua Wu, Min-Hung Chen, Yu-Chiang Frank Wang, Fu-En Yang |
NeurIPS | 2 |
| 2025 | Unified Reinforcement and Imitation Learning for Vision-Language ModelsabstractVision-Language Models (VLMs) have achieved remarkable progress, yet their large scale often renders them impractical for resource-constrained environments. This paper introduces Unified Reinforcement and Imitation Learning (RIL), a novel and efficient training algorithm designed to create powerful, lightweight VLMs. RIL distinctively combines the strengths of reinforcement learning with adversarial imitation learning. This enables smaller student VLMs not only to mimic the sophisticated text generation of large teacher models but also to systematically improve their generative capabilities through reinforcement signals. Key to our imitation framework is a LLM-based discriminator that adeptly distinguishes between student and teacher outputs, complemented by guidance from multiple large teacher VLMs to ensure diverse learning. This unified learning strategy, leveraging both reinforcement and imitation, empowers student models to achieve significant performance gains, making them competitive with leading closed-source VLMs. Extensive experiments on diverse vision-language benchmarks demonstrate that RIL significantly narrows the performance gap with state-of-the-art open- and closed-source VLMs and, in several instances, surpasses them. Ryo Hachiuma, Yong Man Ro, Yu-Chiang Frank Wang, Yueh-Hua Wu |
NeurIPS | 5 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 266 |
| 2023 | Elastic Decision TransformerabstractThis paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to "stitch" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games. Yueh-Hua Wu, Xiaolong Wang 0004, Masashi Hamaya |
NeurIPS | 1 |
| 2022 | DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
Yuzhe Qin, Yueh-Hua Wu, Hanwen Jiang, Ruihan Yang, Xiaolong Wang 0004 |
ECCV (39) | 2 |
| 2019 | Imitation Learning from Imperfect DemonstrationabstractImitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectively learn from imperfect demonstrations, we propose a novel approach that utilizes confidence scores, which describe the quality of demonstrations. More specifically, we propose two confidence-based IL methods, namely two-step importance weighting IL (2IWIL) and generative adversarial IL with imperfect demonstration and confidence (IC-GAIL). We show that confidence scores given only to a small portion of sub-optimal demonstrations significantly improve the performance of IL both theoretically and empirically. Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao 0002, Voot Tangkaratt, Masashi Sugiyama |
ICML | 1 |
| 2018 | A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning AgentsabstractThis paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically. Our model allows the designers of RL agents to solely focus on the task to achieve, without having to worry about the implementation of multiple trivial ethical patterns to follow. Based on the assumption that the majority of human behavior, regardless which goals they are achieving, is ethical, our design integrates human policy with the RL policy to achieve the target objective with less chance of violating the ethical code that human beings normally obey. Yueh-Hua Wu, Shou-De Lin |
AAAI | 1 |
| 2018 | Designing Non-greedy Reinforcement Learning Agents with Diminishing Reward ShapingabstractThis paper intends to address an issue in RL that when agents possessing varying capabilities, most resources may be acquired by stronger agents, leaving the weaker ones "starving". We introduce a simple method to train non-greedy agents in multi-agent reinforcement learning scenarios with nearly no extra cost. Our model can achieve the following goals in designing the non-greedy agent:non-homogeneous equality, only need local information, cost-effective, generalizable and configurable. We propose the idea of diminishing reward that makes the agent feel less satisfied for consecutive rewards obtained. This idea allows the agents to behave less greedy with-out the need to explicitly coding any ethical pattern nor monitor other agents' status. Given our framework, resources distributed more equally without running the risk of reaching homogeneous equality. We designed two games, Gathering Game and Hunter Prey to evaluate the quality of the model. Fan-Yun Sun, Yen-Yu Chang, Yueh-Hua Wu, Shou-De Lin |
AIES | 3 |