Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDPulkit Agrawal
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j1]Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. Trans. Mach. Learn. Res. 2023 (2023)- [c59]Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal:
Is Conditional Generative Modeling all you need for Decision Making? ICLR 2023 - [c58]Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche:
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. ICLR 2023 - [c57]Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal:
Learning to Extrapolate: A Transductive Approach. ICLR 2023 - [c56]Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. ICML 2023: 14096-14113 - [c55]Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal:
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. ICML 2023: 19440-19459 - [c54]Andi Peng, Aviv Netanyahu, Mark K. Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal:
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. ICML 2023: 27630-27641 - [c53]Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal:
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. ICML 2023: 31077-31093 - [c52]Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. ICML 2023: 31800-31851 - [c51]Yandong Ji, Gabriel B. Margolis, Pulkit Agrawal:
DribbleBot: Dynamic Legged Manipulation in the Wild. ICRA 2023: 5155-5162 - [c50]Sameer Pai, Tao Chen, Megha Tippur, Edward H. Adelson, Abhishek Gupta, Pulkit Agrawal:
TactoFind: A Tactile Only System for Object Retrieval. ICRA 2023: 8025-8032 - [i65]Andreea Bobu, Andi Peng, Pulkit Agrawal, Julie Shah, Anca D. Dragan:
Aligning Robot and Human Representations. CoRR abs/2302.01928 (2023) - [i64]Baxi Chong, Di Luo, Tianyu Wang, Gabriel B. Margolis, Juntao He, Pulkit Agrawal, Marin Soljacic, Daniel I. Goldman:
Geometry of contact: contact planning for multi-legged robots via spin models duality. CoRR abs/2302.03019 (2023) - [i63]Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. CoRR abs/2302.13934 (2023) - [i62]Sameer Pai, Tao Chen, Megha Tippur, Edward H. Adelson, Abhishek Gupta, Pulkit Agrawal:
TactoFind: A Tactile Only System for Object Retrieval. CoRR abs/2303.13482 (2023) - [i61]Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris N. Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava:
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies. CoRR abs/2304.00601 (2023) - [i60]Yandong Ji, Gabriel B. Margolis, Pulkit Agrawal:
DribbleBot: Dynamic Legged Manipulation in the Wild. CoRR abs/2304.01159 (2023) - [i59]Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal:
Learning to Extrapolate: A Transductive Approach. CoRR abs/2304.14329 (2023) - [i58]Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. CoRR abs/2305.08842 (2023) - [i57]Boyuan Chen, Chuning Zhu, Pulkit Agrawal, Kaiqing Zhang, Abhishek Gupta:
Self-Supervised Reinforcement Learning that Transfers using Random Features. CoRR abs/2305.17250 (2023) - [i56]Zhang-Wei Hong, Pulkit Agrawal, Rémi Tachet des Combes, Romain Laroche:
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. CoRR abs/2306.13085 (2023) - [i55]Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal:
TGRL: An Algorithm for Teacher Guided Reinforcement Learning. CoRR abs/2307.03186 (2023) - [i54]Anthony Simeonov, Ankit Goyal, Lucas Manuelli, Yen-Chen Lin, Alina Sarmiento, Alberto Rodriguez, Pulkit Agrawal, Dieter Fox:
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement. CoRR abs/2307.04751 (2023) - [i53]Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete:
Neuro-Inspired Efficient Map Building via Fragmentation and Recall. CoRR abs/2307.05793 (2023) - [i52]Andi Peng, Aviv Netanyahu, Mark K. Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal:
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. CoRR abs/2307.06333 (2023) - [i51]Marcel Torne, Max Balsells, Zihan Wang, Samedh Desai, Tao Chen, Pulkit Agrawal, Abhishek Gupta:
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback. CoRR abs/2307.11049 (2023) - [i50]Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal:
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. CoRR abs/2307.12983 (2023) - [i49]Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Josh Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal:
Compositional Foundation Models for Hierarchical Planning. CoRR abs/2309.08587 (2023) - [i48]Meenal Parakh, Alisha Fong, Anthony Simeonov, Abhishek Gupta, Tao Chen, Pulkit Agrawal:
Human-Assisted Continual Robot Learning with Foundation Models. CoRR abs/2309.14321 (2023) - [i47]Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal:
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. CoRR abs/2310.04413 (2023) - [i46]Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete:
Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity. CoRR abs/2310.17537 (2023) - [i45]Andi Peng, Mycal Tucker, Eoin M. Kenny, Noga Zaslavsky, Pulkit Agrawal, Julie Shah:
Human-Guided Complexity-Controlled Abstractions. CoRR abs/2310.17550 (2023) - [i44]Max Balsells, Marcel Torne, Zihan Wang, Samedh Desai, Pulkit Agrawal, Abhishek Gupta:
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback. CoRR abs/2310.20608 (2023) - [i43]Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal:
Learning to See Physical Properties with Active Sensing Motor Policies. CoRR abs/2311.01405 (2023) - 2022
- [c49]Gabriel B. Margolis, Pulkit Agrawal:
Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. CoRL 2022: 22-31 - [c48]Anthony Simeonov, Yilun Du, Yen-Chen Lin, Alberto Rodriguez Garcia, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Pulkit Agrawal:
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields. CoRL 2022: 835-846 - [c47]Jie Xu, Sangwoon Kim, Tao Chen, Alberto Rodriguez Garcia, Pulkit Agrawal, Wojciech Matusik, Shinjiro Sueda:
Efficient Tactile Simulation with Differentiability for Robotic Manipulation. CoRL 2022: 1488-1498 - [c46]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. ICLR 2022 - [c45]Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal:
Topological Experience Replay. ICLR 2022 - [c44]Zhang-Wei Hong, Ge Yang, Pulkit Agrawal:
Bi-linear Value Networks for Multi-goal Reinforcement Learning. ICLR 2022 - [c43]Ge Yang, Anurag Ajay, Pulkit Agrawal:
Overcoming The Spectral Bias of Neural Value Approximation. ICLR 2022 - [c42]Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine:
Offline RL Policies Should Be Trained to be Adaptive. ICML 2022: 7513-7530 - [c41]Aviv Netanyahu, Tianmin Shu, Joshua B. Tenenbaum, Pulkit Agrawal:
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning. ICML 2022: 16480-16495 - [c40]Lara Zlokapa, Yiyue Luo, Jie Xu, Michael Foshey, Kui Wu, Pulkit Agrawal, Wojciech Matusik:
An Integrated Design Pipeline for Tactile Sensing Robotic Manipulators. ICRA 2022: 3136-3142 - [c39]Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal:
Stable Object Reorientation using Contact Plane Registration. ICRA 2022: 6379-6385 - [c38]Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann:
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation. ICRA 2022: 6394-6400 - [c37]Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal:
Stubborn: A Strong Baseline for Indoor Object Navigation. IROS 2022: 3287-3293 - [c36]Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal:
Distributionally Adaptive Meta Reinforcement Learning. NeurIPS 2022 - [c35]Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming intrinsic rewards via constrained optimization. NeurIPS 2022 - [c34]Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal:
Rapid Locomotion via Reinforcement Learning. Robotics: Science and Systems 2022 - [i42]Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal:
Stubborn: A Strong Baseline for Indoor Object Navigation. CoRR abs/2203.07359 (2022) - [i41]Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal:
Topological Experience Replay. CoRR abs/2203.15845 (2022) - [i40]Lara Zlokapa, Yiyue Luo, Jie Xu, Michael Foshey, Kui Wu, Pulkit Agrawal, Wojciech Matusik:
An Integrated Design Pipeline for Tactile Sensing Robotic Manipulators. CoRR abs/2204.07149 (2022) - [i39]Zhang-Wei Hong, Ge Yang, Pulkit Agrawal:
Bilinear value networks. CoRR abs/2204.13695 (2022) - [i38]Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal:
Rapid Locomotion via Reinforcement Learning. CoRR abs/2205.02824 (2022) - [i37]Ge Yang, Anurag Ajay, Pulkit Agrawal:
Overcoming the Spectral Bias of Neural Value Approximation. CoRR abs/2206.04672 (2022) - [i36]Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine:
Offline RL Policies Should be Trained to be Adaptive. CoRR abs/2207.02200 (2022) - [i35]Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal:
Stable Object Reorientation using Contact Plane Registration. CoRR abs/2208.08962 (2022) - [i34]Anurag Ajay, Abhishek Gupta, Dibya Ghosh, Sergey Levine, Pulkit Agrawal:
Distributionally Adaptive Meta Reinforcement Learning. CoRR abs/2210.03104 (2022) - [i33]Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming Intrinsic Rewards via Constrained Optimization. CoRR abs/2211.07627 (2022) - [i32]Anthony Simeonov, Yilun Du, Yen-Chen Lin, Alberto Rodriguez, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Pulkit Agrawal:
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields. CoRR abs/2211.09786 (2022) - [i31]Tao Chen
, Megha Tippur, Siyang Wu, Vikash Kumar, Edward H. Adelson, Pulkit Agrawal:
Visual Dexterity: In-hand Dexterous Manipulation from Depth. CoRR abs/2211.11744 (2022) - [i30]Aviv Netanyahu, Tianmin Shu, Joshua B. Tenenbaum, Pulkit Agrawal:
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning. CoRR abs/2211.15339 (2022) - [i29]Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal:
Is Conditional Generative Modeling all you need for Decision-Making? CoRR abs/2211.15657 (2022) - [i28]Gabriel B. Margolis, Pulkit Agrawal:
Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. CoRR abs/2212.03238 (2022) - 2021
- [c33]Pooya Khorrami, Olga Simek, Brian Cheung, Mark Veillette, Rumen Dangovski, Ileana Rugina, Marin Soljacic, Pulkit Agrawal:
Adapting Deep Learning Models to New Meteorological Contexts Using Transfer Learning. IEEE BigData 2021: 4169-4177 - [c32]Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, Antonio Torralba:
3D Neural Scene Representations for Visuomotor Control. CoRL 2021: 112-123 - [c31]Tao Chen, Jie Xu, Pulkit Agrawal:
A System for General In-Hand Object Re-Orientation. CoRL 2021: 297-307 - [c30]Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal:
Learning to Jump from Pixels. CoRL 2021: 1025-1034 - [c29]Pulkit Agrawal:
The Task Specification Problem. CoRL 2021: 1745-1751 - [c28]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. ICLR 2021 - [c27]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. ICML 2021: 3480-3491 - [c26]Joshua Gruenstein, Tao Chen, Neel Doshi, Pulkit Agrawal:
Residual Model Learning for Microrobot Control. ICRA 2021: 7219-7226 - [c25]Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal:
An End-to-End Differentiable Framework for Contact-Aware Robot Design. Robotics: Science and Systems 2021 - [i27]Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. CoRR abs/2103.10427 (2021) - [i26]Joshua Gruenstein, Tao Chen, Neel Doshi, Pulkit Agrawal:
Residual Model Learning for Microrobot Control. CoRR abs/2104.00631 (2021) - [i25]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. CoRR abs/2106.15612 (2021) - [i24]Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, Antonio Torralba:
3D Neural Scene Representations for Visuomotor Control. CoRR abs/2107.04004 (2021) - [i23]Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal:
An End-to-End Differentiable Framework for Contact-Aware Robot Design. CoRR abs/2107.07501 (2021) - [i22]Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal:
Learning to Jump from Pixels. CoRR abs/2110.15344 (2021) - [i21]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Contrastive Learning. CoRR abs/2111.00899 (2021) - [i20]Tao Chen, Jie Xu, Pulkit Agrawal:
A System for General In-Hand Object Re-Orientation. CoRR abs/2111.03043 (2021) - [i19]Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann:
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation. CoRR abs/2112.05124 (2021) - 2020
- [c24]Eliza Kosoy, Jasmine Collins, David M. Chan, Deepak Pathak, Pulkit Agrawal, Alison Gopnik:
Exploring Exploration: Comparing Children with Agents in Unified Exploration Environments. CogSci 2020 - [c23]Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois Robert Hogan, Joshua B. Tenenbaum, Pulkit Agrawal, Alberto Rodriguez:
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects. CoRL 2020: 1582-1601 - [c22]Tyler B. Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin:
AdaScale SGD: A User-Friendly Algorithm for Distributed Training. ICML 2020: 4911-4920 - [c21]Richard Li, Allan Jabri, Trevor Darrell, Pulkit Agrawal:
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning. ICRA 2020: 4051-4058 - [i18]Tyler B. Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin:
AdaScale SGD: A User-Friendly Algorithm for Distributed Training. CoRR abs/2007.05105 (2020) - [i17]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. CoRR abs/2010.13611 (2020) - [i16]Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois Robert Hogan, Joshua B. Tenenbaum, Pulkit Agrawal, Alberto Rodriguez:
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects. CoRR abs/2011.08177 (2020)
2010 – 2019
- 2019
- [c20]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Tom Griffiths:
Human-level but not human-like: Deep Reinforcement Learning in the dark. CogSci 2019: 3265 - [c19]Eliza Kosoy, Deepak Pathak, Pulkit Agrawal, Alison Gopnik:
Curiouser and Curiouser: Children's intrinsic exploration of mazes and its effects on reaching a goal. CogSci 2019: 3496 - [c18]Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen:
Superposition of many models into one. NeurIPS 2019: 10867-10876 - [c17]Pulkit Agrawal, Rajat Arya, Aanchal Bindal, Sandeep Bhatia, Anupriya Gagneja, Joseph Godlewski, Yucheng Low, Timothy Muss, Mudit Manu Paliwal, Sethu Raman, Vishrut Shah, Bochao Shen, Laura Sugden, Kaiyu Zhao, Ming-Chuan Wu:
Data Platform for Machine Learning. SIGMOD Conference 2019: 1803-1816 - [i15]Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen:
Superposition of many models into one. CoRR abs/1902.05522 (2019) - [i14]Richard Li, Allan Jabri, Trevor Darrell, Pulkit Agrawal:
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning. CoRR abs/1912.11032 (2019) - 2018
- [b1]Pulkit Agrawal:
Computational Sensorimotor Learning. University of California, Berkeley, USA, 2018 - [c16]Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik:
Learning Instance Segmentation by Interaction. CVPR Workshops 2018: 2042-2045 - [c15]Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell:
Zero-Shot Visual Imitation. CVPR Workshops 2018: 2050-2053 - [c14]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Thomas L. Griffiths:
Investigating Human Priors for Playing Video Games. ICLR (Workshop) 2018 - [c13]Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell:
Zero-Shot Visual Imitation. ICLR 2018 - [c12]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei A. Efros:
Investigating Human Priors for Playing Video Games. ICML 2018: 1348-1356 - [i13]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros:
Investigating Human Priors for Playing Video Games. CoRR abs/1802.10217 (2018) - [i12]Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell:
Zero-Shot Visual Imitation. CoRR abs/1804.08606 (2018) - [i11]Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik:
Learning Instance Segmentation by Interaction. CoRR abs/1806.08354 (2018) - 2017
- [c11]Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell:
Curiosity-Driven Exploration by Self-Supervised Prediction. CVPR Workshops 2017: 488-489 - [c10]Panna Felsen, Pulkit Agrawal, Jitendra Malik:
What will Happen Next? Forecasting Player Moves in Sports Videos. ICCV 2017: 3362-3371 - [c9]Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas:
Learning to Perform Physics Experiments via Deep Reinforcement Learning. ICLR (Poster) 2017 - [c8]Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell:
Curiosity-driven Exploration by Self-supervised Prediction. ICML 2017: 2778-2787 - [c7]Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Combining self-supervised learning and imitation for vision-based rope manipulation. ICRA 2017: 2146-2153 - [i10]Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation. CoRR abs/1703.02018 (2017) - [i9]Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell:
Curiosity-driven Exploration by Self-supervised Prediction. CoRR abs/1705.05363 (2017) - [i8]Amir R. Zamir, Tilman Wekel, Pulkit Agrawal, Colin Wei, Jitendra Malik, Silvio Savarese:
Generic 3D Representation via Pose Estimation and Matching. CoRR abs/1710.08247 (2017) - 2016
- [c6]João Carreira, Pulkit Agrawal, Katerina Fragkiadaki, Jitendra Malik:
Human Pose Estimation with Iterative Error Feedback. CVPR 2016: 4733-4742 - [c5]Amir R. Zamir, Tilman Wekel, Pulkit Agrawal, Colin Wei, Jitendra Malik, Silvio Savarese:
Generic 3D Representation via Pose Estimation and Matching. ECCV (3) 2016: 535-553 - [c4]Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik:
Learning Visual Predictive Models of Physics for Playing Billiards. ICLR (Poster) 2016 - [i7]Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine:
Learning to Poke by Poking: Experiential Learning of Intuitive Physics. CoRR abs/1606.07419 (2016) - [i6]Mi-Young Huh, Pulkit Agrawal, Alexei A. Efros:
What makes ImageNet good for transfer learning? CoRR abs/1608.08614 (2016) - [i5]Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas:
Learning to Perform Physics Experiments via Deep Reinforcement Learning. CoRR abs/1611.01843 (2016) - 2015
- [c3]Pulkit Agrawal, João Carreira, Jitendra Malik:
Learning to See by Moving. ICCV 2015: 37-45 - [i4]Pulkit Agrawal, João Carreira, Jitendra Malik:
Learning to See by Moving. CoRR abs/1505.01596 (2015) - [i3]