Demo Data from EDBT 2026 / reviewers in the wild / expert
Stephen Tian
dblp:237/9780
· DBLP profile ↗
11ranked-venue papers
4as first author
8since 2021 · last 2025
0000-0003-3148-5541ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 11 · 4 first-author · 8 since 2021Systems, architecture and hardware · 4 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author · 2 since 2021
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
11 papers |
Robot manipulation · 30% Motion planning and robot control · 21% 3D vision · 18% | |
| Computer graphics and multimedia
2 papers |
Virtual and augmented reality · 70% Computational fabrication · 30% |
Topics — the 24 heaviest of 29, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Video understanding and tracking
video prediction |
1.3 | 2 | 2023 | A Control-Centric Benchmark for Video Prediction · ICLR 2023 MaskViT: Masked Visual Pre-Training for Video Prediction · ICLR 2023 |
Robotics › Motion planning and robot control › robot control
model predictive control |
1.0 | 2 | 2023 | Multi-Object Manipulation via Object-Centric Neural Scattering Functions · CVPR 2023 Manipulation by Feel: Touch-Based Control with Deep Predictive Models · ICRA 2019 |
Computer vision › 3D vision
3d shape reconstruction |
0.9 | 1 | 2025 | Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset · CVPR 2025 |
Robotics › Robot manipulation
digital twin generation |
0.9 | 1 | 2025 | Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset · CVPR 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 |
Robotics › Robot manipulation
grasping |
0.8 | 1 | 2024 | Learning to Design 3D Printable Adaptations on Everyday Objects for Robot Manipulation · ICRA 2024 |
Robotics › Motion planning and robot control
robot learning |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Machine learning › Representation and self-supervised learning › contrastive learning
contrastive feature learning |
0.7 | 1 | 2023 | Are These the Same Apple? Comparing Images Based on Object Intrinsics · NeurIPS 2023 |
Computer vision › 3D vision › 3d scene modeling › scene representation
neural scene representation |
0.7 | 1 | 2023 | Multi-Object Manipulation via Object-Centric Neural Scattering Functions · CVPR 2023 |
Machine learning › Reinforcement learning
model-based reinforcement learning |
0.6 | 2 | 2021 | Model-Based Visual Planning with Self-Supervised Functional Distances · ICLR 2021 Learning Predictive Models from Observation and Interaction · ECCV (20) 2020 |
Robotics › Robot navigation and mapping
visual navigation |
0.5 | 1 | 2021 | Model-Based Visual Planning with Self-Supervised Functional Distances · ICLR 2021 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
visual planning |
0.5 | 1 | 2021 | Model-Based Visual Planning with Self-Supervised Functional Distances · ICLR 2021 |
Robotics › Robot manipulation
dexterous manipulation |
0.4 | 1 | 2020 | OmniTact: A Multi-Directional High-Resolution Touch Sensor · ICRA 2020 |
Robotics › Robot manipulation
learning from demonstration |
0.4 | 1 | 2020 | Learning Predictive Models from Observation and Interaction · ECCV (20) 2020 |
Robotics › Robot manipulation
tactile sensing |
0.4 | 1 | 2020 | OmniTact: A Multi-Directional High-Resolution Touch Sensor · ICRA 2020 |
Robotics › Motion planning and robot control › robot control
model-based control |
0.4 | 1 | 2019 | Manipulation by Feel: Touch-Based Control with Deep Predictive Models · ICRA 2019 |
Robotics › Robot manipulation › dexterous manipulation
tactile manipulation |
0.4 | 1 | 2019 | Manipulation by Feel: Touch-Based Control with Deep Predictive Models · ICRA 2019 |
Robotics › Robot manipulation › tactile sensing › tactile feedback control
tactile servoing |
0.4 | 1 | 2019 | Manipulation by Feel: Touch-Based Control with Deep Predictive Models · ICRA 2019 |
Virtual and augmented reality › augmented reality display
AR glasses |
0.3 | 1 | 2025 | Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset · CVPR 2025 |
Virtual and augmented reality › tracking
egocentric motion capture |
0.3 | 1 | 2025 | Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset · CVPR 2025 |
Computational fabrication
additive manufacturing |
0.2 | 1 | 2024 | Learning to Design 3D Printable Adaptations on Everyday Objects for Robot Manipulation · ICRA 2024 |
Robotics › Robot manipulation › object manipulation
multi-object manipulation |
0.2 | 1 | 2023 | Multi-Object Manipulation via Object-Centric Neural Scattering Functions · CVPR 2023 |
Computer vision › Face, body and person analysis
re-identification |
0.2 | 1 | 2023 | Are These the Same Apple? Comparing Images Based on Object Intrinsics · NeurIPS 2023 |
Robotics › Robot manipulation
nonprehensile manipulation |
0.1 | 1 | 2019 | Manipulation by Feel: Touch-Based Control with Deep Predictive Models · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
neural reconstruction · 1.7inverse rendering · 1.7reinforcement learning · 1.5markov decision process · 1.5transformer policy · 0.8large-scale pretraining · 0.8neural scattering function · 0.7masked modeling · 0.7inverse parameter estimation · 0.7graph-based neural dynamics · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin DatasetabstractWe introduce Digital Twin Catalog (DTC), a new large-scale photorealistic 3D object digital twin dataset. A digital twin of a 3D object is a highly detailed, virtually indistinguishable representation of a physical object, accurately capturing its shape, appearance, physical properties, and other attributes. Recent advances in neural-based 3D reconstruction and inverse rendering have significantly improved the quality of 3D object reconstruction. Despite these advancements, there remains a lack of a large-scale, digital twin quality real-world dataset and benchmark that can quantitatively assess and compare the performance of different reconstruction methods, as well as improve reconstruction quality through training or fine-tuning. Moreover, to democratize 3D digital twin creation, it is essential to integrate creation techniques with next-generation egocentric computing platforms, such as AR glasses. Currently, there is no dataset available to evaluate 3D object reconstruction using egocentric captured images. To address these gaps, the DTC dataset features 2,000 scanned digital twin-quality 3D objects, along with image sequences captured under different lighting conditions using DSLR cameras and egocentric AR glasses. This dataset establishes the first comprehensive real-world evaluation benchmark for 3D digital twin creation tasks, offering a robust foundation for comparing and improving existing reconstruction methods. The DTC dataset is already released at https://www.projectaria.com/datasets/dtc/ and we will also make the baseline evaluations open-source. Zhao Dong 0001, Ka Chen, Zhaoyang Lv, Hong-Xing Yu, Yufeng Zhu, Stephen Tian, Zhengqin Li, Geordie Moffatt, Sean Christofferson, James Fort, Xiaqing Pan, Mingfei Yan, Jiajun Wu 0001, Carl Yuheng Ren, Richard A. Newcombe |
CVPR | 8 |
| 2024 | Learning to Design 3D Printable Adaptations on Everyday Objects for Robot ManipulationabstractAdvancements in robot learning for object manipulation have shown promising results, yet certain everyday objects remain challenging for robots to effectively interact with. This discrepancy arises from the fact that human-designed objects are optimized for human use rather than robot manipulation. To address this gap, we propose a framework to automatically design 3D printable adaptations that can be attached to hard-to-use objects, thus improving "robot ergonomics". Our learning-based framework formulates the adaptation design and control as a dual Markov decision process and is able to improve robot-object interactions for various robot end effectors and objects. We further validate our designs in the real world with a Franka Panda robot. Please see the supplementary video and https://object-adaptation.github.io for additional visualizations. Michelle Guo, Ziang Liu 0008, Stephen Tian, Zhaoming Xie, Jiajun Wu 0001, C. Karen Liu |
ICRA | 3 |
| 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 | 220 |
| 2023 | Multi-Object Manipulation via Object-Centric Neural Scattering FunctionsabstractLearned visual dynamics models have proven effective for robotic manipulation tasks. Yet, it remains unclear how best to represent scenes involving multi-object interactions. Current methods decompose a scene into discrete objects, but they struggle with precise modeling and manipulation amid challenging lighting conditions as they only encode appearance tied with specific illuminations. In this work, we propose using object-centric neural scattering functions (OSFs) as object representations in a model-predictive control framework. OSFs model per-object light transport, enabling compositional scene re-rendering under object rearrangement and varying lighting conditions. By combining this approach with inverse parameter estimation and graph-based neural dynamics models, we demonstrate improved model-predictive control performance and generalization in compositional multi-object environments, even in previously unseen scenarios and harsh lighting conditions. Stephen Tian, Yancheng Cai, Hong-Xing Yu, Sergey Zakharov, Katherine Liu, Adrien Gaidon, Yunzhu Li, Jiajun Wu 0001 |
CVPR | 1 |
| 2023 | MaskViT: Masked Visual Pre-Training for Video Prediction
Agrim Gupta, Stephen Tian, Jiajun Wu 0001, Roberto Martin Martin, Li Fei-Fei 0001 |
ICLR | 2 |
| 2023 | A Control-Centric Benchmark for Video Prediction
Stephen Tian, Chelsea Finn, Jiajun Wu 0001 |
ICLR | 1 |
| 2023 | Are These the Same Apple? Comparing Images Based on Object IntrinsicsabstractThe human visual system can effortlessly recognize an object under different extrinsic factors such as lighting, object poses, and background, yet current computer vision systems often struggle with these variations. An important step to understanding and improving artificial vision systems is to measure image similarity purely based on intrinsic object properties that define object identity. This problem has been studied in the computer vision literature as re-identification, though mostly restricted to specific object categories such as people and cars. We propose to extend it to general object categories, exploring an image similarity metric based on object intrinsics. To benchmark such measurements, we collect the Common paired objects Under differenT Extrinsics (CUTE) dataset of 18, 000 images of 180 objects under different extrinsic factors such as lighting, poses, and imaging conditions. While existing methods such as LPIPS and CLIP scores do not measure object intrinsics well, we find that combining deep features learned from contrastive self-supervised learning with foreground filtering is a simple yet effective approach to approximating the similarity. We conduct an extensive survey of pre-trained features and foreground extraction methods to arrive at a strong baseline that best measures intrinsic object-centric image similarity among current methods. Finally, we demonstrate that our approach can aid in downstream applications such as acting as an analog for human subjects and improving generalizable re-identification. Please see our project website at https://s-tian.github.io/projects/cute/ for visualizations of the data and demos of our metric. Klemen Kotar, Stephen Tian, Hong-Xing Yu, Dan Yamins, Jiajun Wu 0001 |
NeurIPS | 2 |
| 2021 | Model-Based Visual Planning with Self-Supervised Functional Distances
Stephen Tian, Suraj Nair 0003, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine |
ICLR | 1 |
| 2020 | Learning Predictive Models from Observation and Interaction
Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn |
ECCV (20) | 4 |
| 2020 | OmniTact: A Multi-Directional High-Resolution Touch SensorabstractIncorporating touch as a sensing modality for robots can enable finer and more robust manipulation skills. Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. In this paper, we introduce OmniTact, a multi-directional high-resolution tactile sensor. OmniTact is designed to be used as a fingertip for robotic manipulation with robotic hands, and uses multiple micro-cameras to detect multi-directional deformations of a gel-based skin. This provides a rich signal from which a variety of different contact state variables can be inferred using modern image processing and computer vision methods. We evaluate the capabilities of OmniTact on a challenging robotic control task that requires inserting an electrical connector into an outlet, as well as a state estimation problem that is representative of those typically encountered in dexterous robotic manipulation, where the goal is to infer the angle of contact of a curved finger pressing against an object. Both tasks are performed using only touch sensing and deep convolutional neural networks to process images from the sensor's cameras. We compare with a state-of-the-art tactile sensor that is only sensitive on one side, as well as a state-of-the-art multi-directional tactile sensor, and find that OmniTact's combination of high-resolution and multi-directional sensing is crucial for reliably inserting the electrical connector and allows for higher accuracy in the state estimation task. Videos and supplementary material can be found here4. Akhil Padmanabha, Frederik Ebert, Stephen Tian, Roberto Calandra, Chelsea Finn, Sergey Levine |
ICRA | 3 |
| 2019 | Manipulation by Feel: Touch-Based Control with Deep Predictive ModelsabstractTouch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc. Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine |
ICRA | 1 |