Yunliang Chen 0001

VLDB 2026 Research / reviewers in the wild / expert

dblp:90/7405-1 · also Lawrence Yunliang Chen · DBLP profile ↗

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10ranked-venue papers

4as first author

10since 2021 · last 2025

0009-0001-9064-9676ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 9 · 3 first-author · 9 since 2021Systems, architecture and hardware · 8 · 2 first-author · 8 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Year	Publication	Venue	Position
2025	Robo-DM: Data Management for Large Robot Datasets abstract Recent results suggest that very large datasets of teleoperated robot demonstrations can be used to train transformer-based models that have the potential to generalize to new scenes, robots, and tasks. However, curating, distributing, and loading large datasets of robot trajectories, which typically consist of video, textual, and numerical modalities - including streams from multiple cameras - remains challenging. We propose Robo-DM, an efficient open-source cloud-based data management toolkit for collecting, sharing, and learning with robot data. With Robo-DM, robot datasets are stored in a self-contained format with Extensible Binary Meta Language (EBML). Robo-DM can significantly reduce the size of robot trajectory data, transfer costs, and data load time during training. Compared to the RLDS format used in OXE datasets, Robo-DM's compression saves space by up to 70x (lossy) and 3.5x (lossless). Robo-DM also accelerates data retrieval by load-balancing video decoding with memory-mapped decoding caches. Compared to LeRobot, a framework that also uses lossy video compression, Robo-DM is up to 50x faster when decoding sequentially. We physically evaluate a model trained by Robo-DM with lossy compression, a pick-and-place task, and In-Context Robot Transformer. Robo-DM uses 75x compression of the original dataset and does not suffer reduction in downstream task accuracy. Code and evaluation scripts can be found on website https://github.com/BerkeleyAutomation/fog_x. Kaiyuan Chen 0001, Letian Fu, Yunliang Chen 0001, Kush Hari, Ashwin Balakrishna, Ted Xiao, Pannag R. Sanketi, John Kubiatowicz, Kenneth Y. Goldberg	ICRA	5
2025	ICRT: In-Context Imitation Learning via Next-Token Prediction abstract In-context imitation learning is the capability to perform novel tasks when prompted with task demonstration examples. In-Context Robot Transformer (ICRT) is a causal transformer that performs autoregressive prediction on sensorimotor trajectories, which include images, proprioceptive states, and actions. This approach supports flexible and training-free execution of new tasks at test time. Experiments with a Franka Emika robot demonstrate that ICRT can adapt to new environment configurations that differ from both the prompt and the training data. In a multi-task environment setup, ICRT significantly outperforms current state-of-the-art robot foundation models on generalization to unseen tasks. Code, data, and appendix are available on https://icrt.dev. Max Fu, Gaurav Datta, Yunliang Chen 0001, Will Panitch, Fangchen Liu, Kenneth Y. Goldberg	ICRA	4
2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration abstract Large, 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	142
2024	MANIP: A Modular Architecture for Integrating Interactive Perception for Robot Manipulation abstract We propose a modular systems architecture, MANIP, that can facilitate the design and development of robot manipulation systems by systematically combining learned subpolicies with well-established procedural algorithmic primitives such as Inverse Kinematics, Kalman Filters, RANSAC outlier rejection, PID modules, etc. (aka "Good Old Fashioned Engineering (GOFE)"). The MANIP architecture grew from our lab’s experience developing robot systems for folding clothes, routing cables, and untangling knots. To address failure modes, MANIP can facilitate inclusion of "interactive perception" subpolicies that execute robot actions to modify system state to bring the system into alignment with the training distribution and / or to disambiguate system state when system state confidence is low. We demonstrate how MANIP can be applied with 3 case studies and then describe a detailed case study in cable tracing with experiments that suggest MANIP can improve performance by up to 88%. Code and details are available at: https://berkeleyautomation.github.io/MANIP/ Justin Yu, Tara Sadjadpour, Abigail O'Neill, Mehdi Khfifi, Yunliang Chen 0001, Richard Cheng, Muhammad Zubair Irshad, Ashwin Balakrishna, Thomas Kollar, Kenneth Y. Goldberg	IROS	5
2024	Optimal Arrangement and Rearrangement of Objects on Shelves to Minimize Robot Retrieval Cost abstract From residential homes to commercial outlets and industrial warehouses, shelves are widely used to store a variety of objects. We conjecture that optimizing object arrangements can significantly improve the efficiency of searching and retrieving target objects for robots and automation systems. We formalize the Optimal Shelf Arrangement (OSA) problem, which, given a set of objects’ access frequencies and movement costs, optimizes their placement on a shelf to minimize access time. We present, a mixed-integer program (MIP), show that it finds an optimal solution for OSA under certain conditions, and provide bounds on its suboptimality in general cost settings. We further present -Stack, which extends the setting to environments that allow object stacking. Additionally, we consider improving a given suboptimal object arrangement through efficient rearrangement. Experiment results from over 2,700 simulated shelf trials and 54 physical trials using a Fetch robot equipped with a suction grasping tool and pushing blade suggest that an optimal arrangement can reduce the expected retrieval cost by 60–80% in fully-observed configurations and reduce the expected search cost by 50–70% while increasing the search success rate by up to 2x in partially-observed configurations.Note to Practitioners—While there is a large literature on task and motion planning that studies how to rearrange objects from one arrangement to another, there is little research on what an ideal “target” arrangement should be. This article demonstrates that an optimal arrangement of objects makes searching and retrieving objects on shelves significantly easier. We present the Optimal Shelf Arrangement (OSA) problem and algorithms to solve it. Although we assume access to a robot that can push and use a vacuum suction cup gripper, practitioners can adapt the OSA model to their own hardware and shelf constraints and objectives. Also, the algorithms for improving existing shelf arrangements can be applied to scenarios where the objects are already on the shelves rather than to be arranged from scratch. Yunliang Chen 0001, Kenneth Y. Goldberg	IEEE Trans Autom. Sci. Eng.	1
2023	AutoBag: Learning to Open Plastic Bags and Insert Objects abstract Thin plastic bags are ubiquitous in retail stores, healthcare, food handling, recycling, homes, and school lunchrooms. They are challenging both for perception (due to specularities and occlusions) and for manipulation (due to the dynamics of their 3D deformable structure). We formulate the task of “bagging:” manipulating common plastic shopping bags with two handles from an unstructured initial state to an open state where at least one solid object can be inserted into the bag and lifted for transport. We propose a self-supervised learning framework where a dual-arm robot learns to recognize the handles and rim of plastic bags using UV-fluorescent markings; at execution time, the robot does not use UV markings or UV light. We propose the AutoBag algorithm, where the robot uses the learned perception model to open a plastic bag through iterative manipulation. We present novel metrics to evaluate the quality of a bag state and new motion primitives for reorienting and opening bags based on visual observations. In physical experiments, a YuMi robot using AutoBag is able to open bags and achieve a success rate of 16/30 for inserting at least one item across a variety of initial bag configurations. Supplementary material is available at https://sites.google.com/view/autobag. Yunliang Chen 0001, Baiyu Shi, Daniel Seita, Richard Cheng, Thomas Kollar, David Held, Kenneth Y. Goldberg	ICRA	1
2023	Bagging by Learning to Singulate Layers Using Interactive Perception abstract Many fabric handling and 2D deformable material tasks in homes and industries require singulating layers of material such as opening a bag or arranging garments for sewing. In contrast to methods requiring specialized sensing or end effectors, we use only visual observations with ordinary parallel jaw grippers. We propose SLIP: Singulating Layers using Interactive Perception, and apply SLIP to the task of autonomous bagging. We develop SLIP-Bagging, a bagging algorithm that manipulates a plastic or fabric bag from an unstructured state and uses SLIP to grasp the top layer of the bag to open it for object insertion. In physical experiments, a YuMi robot achieves a success rate of 67% to 81% across bags of a variety of materials, shapes, and sizes, significantly improving in success rate and generality over prior work. Experiments also suggest that SLIP can be applied to tasks such as singulating layers of folded cloth and garments. Supplementary material is available at https://sites.google.com/view/slip-bagging/. Yunliang Chen 0001, Baiyu Shi, Roy Lin, Daniel Seita, Ayah Ahmad, Richard Cheng, Thomas Kollar, David Held, Kenneth Y. Goldberg	IROS	1
2022	Real2Sim2Real: Self-Supervised Learning of Physical Single-Step Dynamic Actions for Planar Robot Casting abstract This paper introduces the task of Planar Robot Casting (PRC): where one planar motion of a robot arm holding one end of a cable causes the other end to slide across the plane toward a desired target. PRC allows the cable to reach points beyond the robot workspace and has applications for cable management in homes, warehouses, and factories. To efficiently learn a PRC policy for a given cable, we propose Real2Sim2Real, a self-supervised framework that automatically collects physical trajectory examples to tune parameters of a dynamics simulator using Differential Evolution, generates many simulated examples, and then learns a policy using a weighted combination of simulated and physical data. We evaluate Real2Sim2Real with three simulators, Isaac Gym-segmented, Isaac Gym-hybrid, and PyBullet, two function approximators, Gaussian Processes and Neural Networks (NNs), and three cables with differing stiffness, torsion, and friction. Results with 240 physical trials suggest that the PRC policies can attain median error distance (as % of cable length) ranging from 8 % to 14 %, outperforming baselines and policies trained on only real or only simulated examples. Code, data, and videos are available at https://tinyurl.com/robotcast. Vincent Lim, Yunliang Chen 0001, Jeffrey Ichnowski, Daniel Seita, Michael Laskey, Kenneth Y. Goldberg	ICRA	3
2022	Efficiently Learning Single-Arm Fling Motions to Smooth Garments Yunliang Chen 0001, Ellen R. Novoseller, Daniel Seita, Jeffrey Ichnowski, Michael Laskey, Richard Cheng, Thomas Kollar, Kenneth Y. Goldberg	ISRR	1
2021	A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration abstract We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is achieved with a new suction cup design that contains multiple chambers for air flow. Each chamber connects with its own remote pressure transducer, which enables both absolute and differential pressure measures between chambers. By changing the overall vacuum applied to this smart suction cup, it can perform different functions such as gentle haptic exploration (low pressure) and monitoring breaks in the seal during strong astrictive gripping (high pressure). Haptic exploration of surfaces through sliding and palpation can guide the selection of suction grasp locations and help to identify the local surface geometry. During suction gripping, a trained LSTM network can localize breaks in the suction seal between four quadrants with up to 97% accuracy and detects breaks in the suction seal early enough to avoid total grasp failure. Tae Myung Huh, Kate Sanders 0002, Michael Danielczuk, Monica S. Li, Yunliang Chen 0001, Kenneth Y. Goldberg, Hannah Stuart	IROS	5