Publications

Jianing Qian, Anastasios Panagopoulos, Dinesh Jayaraman (2024). Recasting Generic Pretrained Vision Transformers As Object-Centric Scene Encoders For Manipulation Policies . ICRA.

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Junyao Shi*, Jianing Qian, Yecheng Jason Ma, Dinesh Jayaraman (2024). Plug-And-Play Object-Centric Representations From “What” and “Where” Foundation Models. ICRA.

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Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri, Eric Eaton, Dinesh Jayaraman, Yang Liu, Deva Ramanan, James Hays (2024). ZeroFlow: Fast Zero Label Scene Flow via Distillation. ICLR.

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Zichen Zhang, Yunshuang Li, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Yecheng Jason Ma, Luca Weihs (2024). Universal Visual Decomposer: Long-Horizon Manipulation Made Easy. ICRA.

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Menachem Stern, Sam Dillavou, Dinesh Jayaraman, Douglas J Durian, Andrea J Liu (2024). Training self-learning circuits for power-efficient solutions. Applied Physics Letters (APL) Machine Learning.

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Edward Hu, James Springer, Oleh Rybkin, Dinesh Jayaraman (2024). Privileged Sensing Scaffolds Reinforcement Learning. ICLR.

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Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, James Weimer, Insup Lee (2024). Memory-Consistent Neural Networks for Imitation Learning. ICLR.

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Sriram Narayanan, Dinesh Jayaraman, Manmohan Chandraker (2024). Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport. ICRA.

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Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar (2024). Eureka: Human-Level Reward Design via Coding Large Language Models. ICLR.

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Chuan Wen, Dinesh Jayaraman, Yang Gao (2024). Can Transformers Capture Spatial Relations between Objects?. ICLR.

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Weilin Wan, Zhiyang Dou, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu* (2023). TLControl: Trajectory and Language Control for Human Motion Synthesis. arXiv.

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Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein (2023). Prospective Learning: Principled Extrapolation to the Future. Proceedings of The 2nd Conference on Lifelong Learning Agents.

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Leon Kim, Yunshuang Li, Michael Posa, Dinesh Jayaraman (2023). Vision-Based Contact Localization Without Touch or Force Sensing. CORL.

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Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman (2023). LIV: Language-Image Representations and Rewards for Robotic Control. ICML.

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Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang (2023). VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. ICLR (top 25%).

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Edward Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman (2023). Planning Goals for Exploration. ICLR (top 25 per cent) and CORL 2022 Robot Adaptation Workshop Best Paper Award.

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Yecheng Jason Ma, Kausik Sivakumar, Jason Yen, Osbert Bastani, Dinesh Jayaraman (2023). Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching. L4DC.

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Yinsen Jia, Jingxi Xu, Dinesh Jayaraman, Shuran Song (2023). Learning a Meta-Controller for Dynamic Grasping. arXiv preprint arXiv:2302.08463.

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Kun Huang, Edward Hu, Dinesh Jayaraman (2022). Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning. CORL (best paper award).

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Jianing Qian, Anastasios Panagopoulos, Dinesh Jayaraman (2022). Discovering Deformable Keypoint Pyramids. ECCV.

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Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani (2022). How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $ f $-Advantage Regression. NeurIPS.

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Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani (2022). SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching. In ICML.

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Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao (2022). Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. ICML.

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Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman (2022). Know Thyself: Transferable Visuomotor Control Through Robot-Awareness. In ICLR.

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Joshua T Vogelstein, Timothy Verstynen, Konrad P Kording, Leyla Isik, John W Krakauer, Ralph Etienne-Cummings, Elizabeth L Ogburn, Carey E Priebe, Randal Burns, Kwame Kutten, James J Knierim, James B Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena Savonenko, Ian Phillips, Michael I Miller, Rene Vidal, Jeremias Sulam, Adam Charles, Noah J Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish Thakor, Justus M Kebschull, Marilyn Albert, Jinchong Xu, Marshall Hussain Shuler, Brian Caffo, Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J How, Tyler M Tomita, Jayanta Dey, Ningyuan Huang, Jong M Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael Platt, Lyle Ungar, Leila Wehbe, Adam Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R Muotri, Gabriel Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang (2022). Prospective Learning: Back to the Future. In arXiv.

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Yecheng Jason Ma, Andrew Shen, Osbert Bastani, Dinesh Jayaraman (2022). Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning. AAAI.

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Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman (2021). Keyframe-Focused Visual Imitation Learning. In ICML.

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Hui Chen, Zhao Li, Sheng Feng, Anni Wang, Melissa Richard-Greenblatt, Emily Hutson, Stefen Andrianus, Laurel J. Glaser, Kyle G. Rodino, Jianing Qian, Dinesh Jayaraman, Ronald G. Collman, Abigail Glascock, Frederic D. Bushman, Jae Seung Lee, Sara Cherry, Alejandra Fausto, Susan R. Weiss, Hyun Koo, Patricia M. Corby, Una O'Doherty, Alfred L. Garfall, Dan T. Vogl, Edward A. Stadtmauer, Ping Wang (2021). Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking. Clinical Chemistry.

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Yecheng Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman, Osbert Bastani (2021). Likelihood-Based Diverse Sampling for Trajectory Forecasting. ICCV.

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Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani (2021). Conservative Offline Distributional Reinforcement Learning. In NeurIPS.

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Jingxi Xu, Bruce Lee, Nikolai Matni, Dinesh Jayaraman (2021). How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?. L4DC.

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Santhosh K Ramakrishnan*, Dinesh Jayaraman, Kristen Grauman (2021). An Exploration of Embodied Visual Exploration. In IJCV.

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Glen Berseth, Daniel Geng, Coline Devin, Chelsea Finn, Dinesh Jayaraman, Sergey Levine (2021). SMIRL: Surprise Minimizing RL in Dynamic Environments. In ICLR.

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Jianing Qian, Dinesh Jayaraman (2021). Object Representations Guided By Optical Flow. NeurIPS Robot Learning Workshop.

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Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman, Kostas Daniilidis (2021). Embracing the Reconstruction Uncertainty in 3D Human Pose Estimation. ICCV.

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Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier (2020). Model-Based Inverse Reinforcement Learning from Visual Demonstrations. In CORL.

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Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao (2020). Fighting Copycat Agents in Behavioral Cloning from Multiple Observations.. In NeurIPS.

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Karl Pertsch*, Oleg Rybkin*, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine (2020). Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. In NeurIPS.

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Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman (2020). Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. ICML.

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Brian Yang, Dinesh Jayaraman, Glen Berseth, Alexei Efros, Sergey Levine (2020). MAVRIC: Morphology-Agnostic Visual Robotic Control. In ICRA and RA-L.

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Mike Lambeta, Po-Wei Chou, Stephen Tian, Brian Yang, Benjamin Maloon, Victoria Rose Most, Dave Stroud, Raymond Santos, Ahmad Byagowi, Gregg Kammerer, Dinesh Jayaraman, Roberto Calandra (2020). DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation. In ICRA and RA-L.

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Pim de Haan, Dinesh Jayaraman, Sergey Levine (2019). Causal Confusion in Imitation Learning. NeurIPS (Oral).

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Dinesh Jayaraman, Frederik Ebert, Alexei A Efros, Sergey Levine (2019). Time-Agnostic Prediction: Predicting Predictable Video Frames. In ICLR.

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Brian Yang, Jesse Zhang, Dinesh Jayaraman, Sergey Levine (2019). REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning. In ICRA.

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Brian Yang, Jesse Zhang, Vitchyr Pong, Sergey Levine, Dinesh Jayaraman (2019). REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning. arXiv preprint (extended version of earlier ICRA publication).

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Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine (2019). Manipulation by Feel: Touch-Based Control with Deep Predictive Models. In ICRA.

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Santhosh K Ramakrishnan*, Dinesh Jayaraman, Kristen Grauman (2019). Emergence of Exploratory Look-Around Behaviors Through Active Observation Completion. In Science Robotics.

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Tao Ma, Wei Sun, Oscar Nestares, Kalpana Seshadrinathan, Dinesh Jayaraman (2018). Techniques for Rectification of Camera Arrays.

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Dinesh Jayaraman, Ruohan Gao, Kristen Grauman (2018). ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids. In ECCV.

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Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H Adelson, Sergey Levine (2018). More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch. In IROS and RAL (RAL Best Paper Runner-Up).

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Dinesh Jayaraman, Kristen Grauman (2018). Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks. In CVPR.

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Dinesh Jayaraman, Kristen Grauman (2018). End-to-End Policy Learning For Active Visual Categorization. In IEEE TPAMI.

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Oscar Nestares, Kalpana Seshadrinathan, Dinesh Jayaraman (2017). Techniques for Improved Focusing of Camera Arrays.

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Dinesh Jayaraman, Kristen Grauman (2017). Learning Image Representations Tied to Egomotion from Unlabeled Video. In IJCV Special Issue of Best Papers from ICCV 2015.

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Dinesh Jayaraman (2017). Embodied Learning for Visual Recognition. The University of Texas at Austin.

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Dinesh Jayaraman, Chao-Yeh Chen*, Fei Sha, Kristen Grauman (2017). Divide, Share, and Conquer: Multi-Task Attribute Learning With Selective Sharing. In Springer Book on Visual Attributes.

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Dinesh Jayaraman, Kristen Grauman (2016). Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video. In CVPR (Oral Spotlight).

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Yu-Chuan Su, Dinesh Jayaraman, Kristen Grauman (2016). Pano2Vid: Automatic Cinematography For Watching 360-degree Videos. In ACCV (Songde Ma Best Application Paper Award).

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Ruohan Gao, Dinesh Jayaraman, Kristen Grauman (2016). Object-Centric Representation Learning from Unlabeled Videos. In ACCV.

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Dinesh Jayaraman, Kristen Grauman (2016). Look-Ahead Before You Leap: End-to-End Active Recognition By Forecasting the Effect of Motion. In ECCV (Oral).

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Dinesh Jayaraman, Kristen Grauman (2015). Learning Image Representations Tied to Egomotion. In ICCV (Oral).

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Dinesh Jayaraman, Kristen Grauman (2014). Zero-Shot Recognition With Unreliable Attributes. In NIPS.

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Dinesh Jayaraman, Fei Sha, Kristen Grauman (2014). Decorrelating Semantic Visual Attributes by Resisting the Urge to Share. In CVPR (Oral).

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Dinesh Jayaraman, Anish Mittal, Anush K Moorthy, Alan C Bovik (2012). Objective Quality Assessment of Multiply Distorted Images. In ASILOMAR Conference on Signals, Systems and Computers.

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