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2020 – today
- 2023
[c36]Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman:
Planning Goals for Exploration. ICLR 2023- [c35]Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang:
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. ICLR 2023 - [c34]Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman:
LIV: Language-Image Representations and Rewards for Robotic Control. ICML 2023: 23301-23320 - [c33]Yecheng Jason Ma, Kausik Sivakumar, Jason Yan, Osbert Bastani, Dinesh Jayaraman:
Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching. L4DC 2023: 259-271 - [i46]Yinsen Jia, Jingxi Xu, Dinesh Jayaraman, Shuran Song:
Learning a Meta-Controller for Dynamic Grasping. CoRR abs/2302.08463 (2023) - [i45]Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman:
Planning Goals for Exploration. CoRR abs/2303.13002 (2023) - [i44]Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri, Eric Eaton, Dinesh Jayaraman, Yang Liu, Deva Ramanan, James Hays:
ZeroFlow: Fast Zero Label Scene Flow via Distillation. CoRR abs/2305.10424 (2023) - [i43]Yecheng Jason Ma, Kausik Sivakumar, Jason Yan, Osbert Bastani, Dinesh Jayaraman:
TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching. CoRR abs/2305.12663 (2023) - [i42]Yecheng Jason Ma, William Liang, Vaidehi Som, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman:
LIV: Language-Image Representations and Rewards for Robotic Control. CoRR abs/2306.00958 (2023) - [i41]Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, James Weimer, Insup Lee:
Memory-Consistent Neural Networks for Imitation Learning. CoRR abs/2310.06171 (2023) - [i40]Zichen Zhang, Yunshuang Li, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Yecheng Jason Ma, Luca Weihs:
Universal Visual Decomposer: Long-Horizon Manipulation Made Easy. CoRR abs/2310.08581 (2023) - [i39]Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar:
Eureka: Human-Level Reward Design via Coding Large Language Models. CoRR abs/2310.12931 (2023) - [i38]Weilin Wan, Zhiyang Dou, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu:
TLControl: Trajectory and Language Control for Human Motion Synthesis. CoRR abs/2311.17135 (2023) - 2022
- [c32]Yecheng Jason Ma, Andrew Shen, Osbert Bastani, Dinesh Jayaraman:
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning. AAAI 2022: 5404-5412 - [c31]Kun Huang, Edward S. Hu, Dinesh Jayaraman:
Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning. CoRL 2022: 11-21 - [c30]Jianing Qian, Anastasios Panagopoulos, Dinesh Jayaraman:
Discovering Deformable Keypoint Pyramids. ECCV (26) 2022: 545-561 - [c29]Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman:
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness. ICLR 2022 - [c28]Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani:
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching. ICML 2022: 14639-14663 - [c27]Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao:
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. ICML 2022: 23723-23750 - [c26]Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani:
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression. NeurIPS 2022 - [i37]Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. 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 H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang:
Prospective Learning: Back to the Future. CoRR abs/2201.07372 (2022) - [i36]Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani:
SMODICE: Versatile Offline Imitation Learning via State Occupancy Matching. CoRR abs/2202.02433 (2022) - [i35]Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani:
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage Regression. CoRR abs/2206.03023 (2022) - [i34]Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao:
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. CoRR abs/2206.10816 (2022) - [i33]Elijah S. Lee, Giuseppe Loianno, Dinesh Jayaraman, Vijay Kumar:
Vision-based Perimeter Defense via Multiview Pose Estimation. CoRR abs/2209.12136 (2022) - [i32]Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang:
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. CoRR abs/2210.00030 (2022) - [i31]Sriram Narayanan, Dinesh Jayaraman, Manmohan Chandraker:
Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport. CoRR abs/2210.15908 (2022) - [i30]Kun Huang, Edward S. Hu, Dinesh Jayaraman:
Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning. CoRR abs/2212.08961 (2022) - 2021
- [j7]Santhosh K. Ramakrishnan
, Dinesh Jayaraman, Kristen Grauman:
An Exploration of Embodied Visual Exploration. Int. J. Comput. Vis. 129(5): 1616-1649 (2021) - [c25]Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman, Kostas Daniilidis:
Probabilistic Modeling for Human Mesh Recovery. ICCV 2021: 11585-11594 - [c24]Yecheng Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman, Osbert Bastani:
Likelihood-Based Diverse Sampling for Trajectory Forecasting. ICCV 2021: 13259-13268 - [c23]Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. ICLR 2021 - [c22]Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman:
Keyframe-Focused Visual Imitation Learning. ICML 2021: 11123-11133 - [c21]Jingxi Xu, Bruce D. Lee, Nikolai Matni, Dinesh Jayaraman:
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control? L4DC 2021: 954-966 - [c20]Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani:
Conservative Offline Distributional Reinforcement Learning. NeurIPS 2021: 19235-19247 - [i29]Jingxi Xu, Bruce D. Lee, Nikolai Matni, Dinesh Jayaraman:
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control? CoRR abs/2104.00827 (2021) - [i28]Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman:
Keyframe-Focused Visual Imitation Learning. CoRR abs/2106.06452 (2021) - [i27]Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani:
Conservative Offline Distributional Reinforcement Learning. CoRR abs/2107.06106 (2021) - [i26]Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman:
Know Thyself: Transferable Visuomotor Control Through Robot-Awareness. CoRR abs/2107.09047 (2021) - [i25]Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman, Kostas Daniilidis:
Probabilistic Modeling for Human Mesh Recovery. CoRR abs/2108.11944 (2021) - [i24]Yecheng Jason Ma, Andrew Shen, Osbert Bastani, Dinesh Jayaraman:
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning. CoRR abs/2112.07701 (2021) - 2020
- [j6]Brian H. Yang, Dinesh Jayaraman, Glen Berseth, Alexei A. Efros, Sergey Levine:
Morphology-Agnostic Visual Robotic Control. IEEE Robotics Autom. Lett. 5(2): 766-773 (2020) - [j5]Mike Lambeta, Po-Wei Chou, Stephen Tian, Brian H. Yang, Benjamin Maloon, Victoria Rose Most, Dave Stroud, Raymond Santos, Ahmad Byagowi, Gregg Kammerer, Dinesh Jayaraman, Roberto Calandra:
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor With Application to In-Hand Manipulation. IEEE Robotics Autom. Lett. 5(3): 3838-3845 (2020) - [c19]Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier:
Model-Based Inverse Reinforcement Learning from Visual Demonstrations. CoRL 2020: 1930-1942 - [c18]Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. ICML 2020: 11055-11065 - [c17]Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine:
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. NeurIPS 2020 - [c16]Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao:
Fighting Copycat Agents in Behavioral Cloning from Observation Histories. NeurIPS 2020 - [i23]Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman:
An Exploration of Embodied Visual Exploration. CoRR abs/2001.02192 (2020) - [i22]Mike Lambeta, Po-Wei Chou, Stephen Tian, Brian H. Yang, Benjamin Maloon, Victoria Rose Most, Dave Stroud, Raymond Santos, Ahmad Byagowi, Gregg Kammerer, Dinesh Jayaraman, Roberto Calandra:
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation. CoRR abs/2005.14679 (2020) - [i21]Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. CoRR abs/2006.13205 (2020) - [i20]Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. CoRR abs/2008.06622 (2020) - [i19]Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier:
Model-Based Inverse Reinforcement Learning from Visual Demonstrations. CoRR abs/2010.09034 (2020) - [i18]Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao:
Fighting Copycat Agents in Behavioral Cloning from Observation Histories. CoRR abs/2010.14876 (2020) - [i17]Yecheng Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman, Osbert Bastani:
Diverse Sampling for Normalizing Flow Based Trajectory Forecasting. CoRR abs/2011.15084 (2020)
2010 – 2019
- 2019
- [j4]Dinesh Jayaraman, Kristen Grauman:
End-to-End Policy Learning for Active Visual Categorization. IEEE Trans. Pattern Anal. Mach. Intell. 41(7): 1601-1614 (2019) - [j3]Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman:
Emergence of exploratory look-around behaviors through active observation completion. Sci. Robotics 4(30) (2019) - [c15]Dinesh Jayaraman, Frederik Ebert, Alexei A. Efros, Sergey Levine:
Time-Agnostic Prediction: Predicting Predictable Video Frames. ICLR (Poster) 2019 - [c14]Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine:
Manipulation by Feel: Touch-Based Control with Deep Predictive Models. ICRA 2019: 818-824 - [c13]Brian H. Yang, Dinesh Jayaraman, Jesse Zhang, Sergey Levine:
REPLAB: A Reproducible Low-Cost Arm Benchmark for Robotic Learning. ICRA 2019: 8691-8697 - [c12]Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. NeurIPS 2019: 11693-11704 - [i16]Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine:
Manipulation by Feel: Touch-Based Control with Deep Predictive Models. CoRR abs/1903.04128 (2019) - [i15]Brian H. Yang, Jesse Zhang, Vitchyr Pong, Sergey Levine, Dinesh Jayaraman:
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning. CoRR abs/1905.07447 (2019) - [i14]Pim de Haan, Dinesh Jayaraman, Sergey Levine:
Causal Confusion in Imitation Learning. CoRR abs/1905.11979 (2019) - [i13]Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman:
Emergence of Exploratory Look-Around Behaviors through Active Observation Completion. CoRR abs/1906.11407 (2019) - [i12]Glen Berseth, Daniel Geng, Coline Devin, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
SMiRL: Surprise Minimizing RL in Dynamic Environments. CoRR abs/1912.05510 (2019) - [i11]Brian H. Yang, Dinesh Jayaraman, Glen Berseth, Alexei A. Efros, Sergey Levine:
Morphology-Agnostic Visual Robotic Control. CoRR abs/1912.13360 (2019) - 2018
- [j2]Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson, Sergey Levine:
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch. IEEE Robotics Autom. Lett. 3(4): 3300-3307 (2018) - [c11]Dinesh Jayaraman, Kristen Grauman:
Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks. CVPR 2018: 1238-1247 - [c10]Dinesh Jayaraman, Ruohan Gao, Kristen Grauman:
ShapeCodes: Self-supervised Feature Learning by Lifting Views to Viewgrids. ECCV (16) 2018: 126-144 - [i10]Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson, Sergey Levine:
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch. CoRR abs/1805.11085 (2018) - [i9]Dinesh Jayaraman, Frederik Ebert, Alexei A. Efros, Sergey Levine:
Time-Agnostic Prediction: Predicting Predictable Video Frames. CoRR abs/1808.07784 (2018) - 2017
- [j1]Dinesh Jayaraman, Kristen Grauman:
Learning Image Representations Tied to Egomotion from Unlabeled Video. Int. J. Comput. Vis. 125(1-3): 136-161 (2017) - [c9]Yu-Chuan Su, Dinesh Jayaraman, Kristen Grauman:
Pano2Vid: Automatic Cinematography for Watching 360° Videos. WICED@Eurographics 2017: 45 - [i8]Dinesh Jayaraman, Ruohan Gao, Kristen Grauman:
Unsupervised learning through one-shot image-based shape reconstruction. CoRR abs/1709.00505 (2017) - [i7]Dinesh Jayaraman, Kristen Grauman:
Learning to look around. CoRR abs/1709.00507 (2017) - 2016
- [c8]Yu-Chuan Su, Dinesh Jayaraman, Kristen Grauman:
Pano2Vid: Automatic Cinematography for Watching 360° Videos. ACCV (4) 2016: 154-171 - [c7]Ruohan Gao, Dinesh Jayaraman, Kristen Grauman:
Object-Centric Representation Learning from Unlabeled Videos. ACCV (5) 2016: 248-263 - [c6]Dinesh Jayaraman, Kristen Grauman:
Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video. CVPR 2016: 3852-3861 - [c5]Dinesh Jayaraman, Kristen Grauman:
Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion. ECCV (5) 2016: 489-505 - [i6]Dinesh Jayaraman, Kristen Grauman:
Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion. CoRR abs/1605.00164 (2016) - [i5]Ruohan Gao, Dinesh Jayaraman, Kristen Grauman:
Object-Centric Representation Learning from Unlabeled Videos. CoRR abs/1612.00500 (2016) - [i4]Yu-Chuan Su, Dinesh Jayaraman, Kristen Grauman:
Pano2Vid: Automatic Cinematography for Watching 360° Videos. CoRR abs/1612.02335 (2016) - 2015
- [c4]Dinesh Jayaraman, Kristen Grauman:
Learning Image Representations Tied to Ego-Motion. ICCV 2015: 1413-1421 - [i3]Dinesh Jayaraman, Kristen Grauman:
Learning image representations equivariant to ego-motion. CoRR abs/1505.02206 (2015) - [i2]Dinesh Jayaraman, Kristen Grauman:
Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video. CoRR abs/1506.04714 (2015) - 2014
- [c3]Dinesh Jayaraman, Fei Sha, Kristen Grauman:
Decorrelating Semantic Visual Attributes by Resisting the Urge to Share. CVPR 2014: 1629-1636 - [c2]Dinesh Jayaraman, Kristen Grauman:
Zero-shot recognition with unreliable attributes. NIPS 2014: 3464-3472 - [i1]Dinesh Jayaraman, Kristen Grauman:
Zero Shot Recognition with Unreliable Attributes. CoRR abs/1409.4327 (2014) - 2012
- [c1]Dinesh Jayaraman, Anish Mittal, Anush K. Moorthy, Alan C. Bovik:
Objective quality assessment of multiply distorted images. ACSCC 2012: 1693-1697
Coauthor Index
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