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Nicolas Heess
Nicolas Manfred Otto Heess
Person information
- affiliation: University College London, Centre for Computational Statistics and Machine Learning
- affiliation: University of Edinburgh, Institute for Adaptive and Neural Computation
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2020 – today
- 2024
- [j11]Tuomas Haarnoja
, Ben Moran
, Guy Lever
, Sandy H. Huang
, Dhruva Tirumala, Jan Humplik, Markus Wulfmeier
, Saran Tunyasuvunakool
, Noah Y. Siegel
, Roland Hafner
, Michael Bloesch
, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever
, Yuval Tassa
, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game
, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber
, Nicole Hurley, Francesco Nori
, Raia Hadsell
, Nicolas Heess
:
Learning agile soccer skills for a bipedal robot with deep reinforcement learning. Sci. Robotics 9(89) (2024) - [j10]Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess:
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. Trans. Mach. Learn. Res. 2024 (2024) - [c98]Noah Y. Siegel, Oana-Maria Camburu, Nicolas Heess, María Pérez-Ortiz:
The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models. ACL (Short Papers) 2024: 530-546 - [c97]Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess:
Neural Population Learning beyond Symmetric Zero-Sum Games. AAMAS 2024: 1247-1255 - [c96]Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess:
NfgTransformer: Equivariant Representation Learning for Normal-form Games. ICLR 2024 - [c95]Dhruva Tirumala, Thomas Lampe, José Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier:
Replay across Experiments: A Natural Extension of Off-Policy RL. ICLR 2024 - [c94]Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel:
Genie: Generative Interactive Environments. ICML 2024 - [c93]Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter:
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs. ICML 2024 - [c92]Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller:
Offline Actor-Critic Reinforcement Learning Scales to Large Models. ICML 2024 - [c91]Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alexander Herzog, 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, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, 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, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su, Haoshu Fang, Haochen Shi, 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, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi Jim 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, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Ning Liu, Norman Di Palo, Nur Muhammad (Mahi) Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, 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, 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, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Liangwei Xu, Xuanlin Li, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Lin:
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. ICRA 2024: 6892-6903 - [c90]Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin A. Riedmiller:
Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. ICRA 2024: 7772-7779 - [c89]Wenhao Yu, Ken Caluwaerts, Atil Iscen, J. Chase Kew, Tingnan Zhang, Daniel Freeman, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, José Enrique Chen, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Ofir Nachum, Ken Oslund, Francesco Romano, Fereshteh Sadeghi, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan, Kuang-Huei Lee:
The Design of the Barkour Benchmark for Robot Agility. IROS 2024: 6818-6825 - [c88]Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng, Maria Attarian, Maria Bauzá Villalonga, Matthew Bennice, Alex Bewley, Adil Dostmohamed, Chuyuan Fu, Nimrod Gileadi, Marissa Giustina, Keerthana Gopalakrishnan, Leonard Hasenclever, Jan Humplik, Jasmine Hsu, Nikhil J. Joshi, Ben Jyenis, J. Chase Kew, Sean Kirmani, Tsang-Wei Edward Lee, Kuang-Huei Lee, Assaf Hurwitz Michaely, Joss Moore, Kenneth Oslund, Dushyant Rao, Allen Z. Ren, Baruch Tabanpour, Quan Vuong, Ayzaan Wahid, Ted Xiao, Ying Xu, Vincent Zhuang, Peng Xu, Erik Frey, Ken Caluwaerts, Tingnan Zhang, Brian Ichter, Jonathan Tompson, Leila Takayama, Vincent Vanhoucke, Izhak Shafran, Maja J. Mataric, Dorsa Sadigh, Nicolas Heess, Kanishka Rao, Nik Stewart, Jie Tan, Carolina Parada:
Learning to Learn Faster from Human Feedback with Language Model Predictive Control. Robotics: Science and Systems 2024 - [d2]Tuomas Haarnoja
, Ben Moran
, Guy Lever
, Sandy H. Huang
, Dhruva Tirumala, Jan Humplik, Markus Wulfmeier
, Saran Tunyasuvunakool
, Noah Y. Siegel
, Roland Hafner
, Michael Bloesch
, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa
, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game
, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber
, Nicole Hurley, Francesco Nori
, Raia Hadsell
, Nicolas Heess
:
Data Release for: Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. Zenodo, 2024 - [i131]Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess:
Neural Population Learning beyond Symmetric Zero-sum Games. CoRR abs/2401.05133 (2024) - [i130]Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller:
Offline Actor-Critic Reinforcement Learning Scales to Large Models. CoRR abs/2402.05546 (2024) - [i129]Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter:
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs. CoRR abs/2402.07872 (2024) - [i128]Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess:
NfgTransformer: Equivariant Representation Learning for Normal-form Games. CoRR abs/2402.08393 (2024) - [i127]Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng, Montserrat Gonzalez Arenas, Maria Attarian, Maria Bauzá, Matthew Bennice, Alex Bewley, Adil Dostmohamed, Chuyuan Kelly Fu, Nimrod Gileadi, Marissa Giustina, Keerthana Gopalakrishnan, Leonard Hasenclever, Jan Humplik, Jasmine Hsu, Nikhil J. Joshi, Ben Jyenis, J. Chase Kew, Sean Kirmani, Tsang-Wei Edward Lee, Kuang-Huei Lee, Assaf Hurwitz Michaely, Joss Moore, Ken Oslund, Dushyant Rao, Allen Z. Ren, Baruch Tabanpour, Quan Vuong, Ayzaan Wahid, Ted Xiao, Ying Xu, Vincent Zhuang, Peng Xu, Erik Frey, Ken Caluwaerts, Tingnan Zhang, Brian Ichter, Jonathan Tompson, Leila Takayama, Vincent Vanhoucke, Izhak Shafran, Maja J. Mataric, Dorsa Sadigh, Nicolas Heess, Kanishka Rao, Nik Stewart, Jie Tan, Carolina Parada:
Learning to Learn Faster from Human Feedback with Language Model Predictive Control. CoRR abs/2402.11450 (2024) - [i126]Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel:
Genie: Generative Interactive Environments. CoRR abs/2402.15391 (2024) - [i125]Noah Y. Siegel, Oana-Maria Camburu, Nicolas Heess, María Pérez-Ortiz:
The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models. CoRR abs/2404.03189 (2024) - [i124]Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy H. Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin A. Riedmiller, Nicolas Heess:
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. CoRR abs/2405.02425 (2024) - [i123]Yusheng Jiao, Feng Ling, Sina Heydari
, Nicolas Heess, Josh Merel, Eva Kanso:
Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice. CoRR abs/2405.11457 (2024) - [i122]Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney:
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR abs/2406.02035 (2024) - [i121]Maria Bauzá, José Enrique Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin A. Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess:
DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots. CoRR abs/2409.06613 (2024) - [i120]Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Rishabh Joshi, Junhyuk Oh, Michael Bloesch, Thomas Lampe, Nicolas Heess, Jonas Buchli, Martin A. Riedmiller:
Preference Optimization as Probabilistic Inference. CoRR abs/2410.04166 (2024) - 2023
- [j9]Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin A. Riedmiller:
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration. Trans. Mach. Learn. Res. 2023 (2023) - [c87]Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb:
Representation Learning in Deep RL via Discrete Information Bottleneck. AISTATS 2023: 8699-8722 - [c86]Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montserrat Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia:
Language to Rewards for Robotic Skill Synthesis. CoRL 2023: 374-404 - [c85]Dianbo Liu, Vedant Shah, Oussama Boussif
, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio:
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. ICLR 2023 - [c84]Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar:
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. ICLR 2023 - [c83]Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess:
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields. ICRA 2023: 9362-9369 - [c82]Joe Watson, Sandy H. Huang, Nicolas Heess:
Coherent Soft Imitation Learning. NeurIPS 2023 - [i119]Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin A. Riedmiller:
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains. CoRR abs/2302.12617 (2023) - [i118]Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar:
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. CoRR abs/2304.06600 (2023) - [i117]Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Markus Wulfmeier, Jan Humplik, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess:
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. CoRR abs/2304.13653 (2023) - [i116]Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin A. Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess:
A Generalist Dynamics Model for Control. CoRR abs/2305.10912 (2023) - [i115]Ken Caluwaerts, Atil Iscen, J. Chase Kew, Wenhao Yu, Tingnan Zhang, Daniel Freeman, Kuang-Huei Lee, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, José Enrique Chen, Omar Cortes, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Linda Luu, Ofir Nachum, Ken Oslund, Jason Powell, Diego Reyes, Francesco Romano, Fereshteh Sadeghi, Ron Sloat, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan:
Barkour: Benchmarking Animal-level Agility with Quadruped Robots. CoRR abs/2305.14654 (2023) - [i114]Joe Watson, Sandy H. Huang, Nicolas Heess:
Coherent Soft Imitation Learning. CoRR abs/2305.16498 (2023) - [i113]Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia:
Language to Rewards for Robotic Skill Synthesis. CoRR abs/2306.08647 (2023) - [i112]Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauzá, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo F. Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess:
RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation. CoRR abs/2306.11706 (2023) - [i111]Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin A. Riedmiller:
Towards A Unified Agent with Foundation Models. CoRR abs/2307.09668 (2023) - [i110]Shruti Mishra, Ankit Anand, Jordan Hoffmann, Nicolas Heess, Martin A. Riedmiller, Abbas Abdolmaleki, Doina Precup:
Policy composition in reinforcement learning via multi-objective policy optimization. CoRR abs/2308.15470 (2023) - [i109]Zhe Wang, Petar Velickovic, Daniel Hennes, Nenad Tomasev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, William Spearman, Ian Graham, Jerome T. Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess, Michael Bowling, Demis Hassabis, Karl Tuyls:
TacticAI: an AI assistant for football tactics. CoRR abs/2310.10553 (2023) - [i108]Dhruva Tirumala, Thomas Lampe, José Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier:
Replay across Experiments: A Natural Extension of Off-Policy RL. CoRR abs/2311.15951 (2023) - [i107]Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle, Karol Hausman, Nicolas Heess:
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities. CoRR abs/2312.01939 (2023) - [i106]Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin A. Riedmiller:
Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. CoRR abs/2312.11374 (2023) - 2022
- [j8]Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess:
Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. 23: 221:1-221:68 (2022) - [j7]Siqi Liu
, Guy Lever
, Zhe Wang
, Josh Merel, S. M. Ali Eslami, Daniel Hennes
, Wojciech M. Czarnecki
, Yuval Tassa
, Shayegan Omidshafiei, Abbas Abdolmaleki
, Noah Y. Siegel
, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool
, H. Francis Song, Markus Wulfmeier
, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey
, Karl Tuyls
, Thore Graepel, Nicolas Heess
:
From motor control to team play in simulated humanoid football. Sci. Robotics 7(69) (2022) - [j6]Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas:
A Generalist Agent. Trans. Mach. Learn. Res. 2022 (2022) - [c81]Wenxuan Zhou, Steven Bohez, Jan Humplik, Nicolas Heess, Abbas Abdolmaleki, Dushyant Rao, Markus Wulfmeier, Tuomas Haarnoja:
Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data. CoLLAs 2022: 294-309 - [c80]Sasha Salter, Markus Wulfmeier, Dhruva Tirumala, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell, Dushyant Rao:
MO2: Model-Based Offline Options. CoLLAs 2022: 902-919 - [c79]Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez:
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. ICLR 2022 - [c78]Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin A. Riedmiller:
Evaluating Model-Based Planning and Planner Amortization for Continuous Control. ICLR 2022 - [c77]Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel:
NeuPL: Neural Population Learning. ICLR 2022 - [c76]Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell:
Learning transferable motor skills with hierarchical latent mixture policies. ICLR 2022 - [c75]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. ICML 2022: 7740-7765 - [c74]Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess:
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. ICML 2022: 13793-13806 - [c73]Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, Sergey Levine:
Offline Meta-Reinforcement Learning for Industrial Insertion. ICRA 2022: 6386-6393 - [c72]Philémon Brakel, Steven Bohez, Leonard Hasenclever, Nicolas Heess, Konstantinos Bousmalis:
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. IROS 2022: 10335-10342 - [c71]Alexandre Galashov, Joshua Scott Merel, Nicolas Heess:
Data augmentation for efficient learning from parametric experts. NeurIPS 2022 - [d1]Siqi Liu
, Guy Lever
, Zhe Wang
, Josh Merel, S. M. Ali Eslami, Daniel Hennes
, Wojciech Czarnecki
, Yuval Tassa
, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel
, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool
, H. Francis Song, Markus Wulfmeier
, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls
, Thore Graepel, Nicolas Heess
:
Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". Zenodo, 2022 - [i105]Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel:
NeuPL: Neural Population Learning. CoRR abs/2202.07415 (2022) - [i104]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. CoRR abs/2202.08417 (2022) - [i103]Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel, Fereshteh Sadeghi, Leonard Hasenclever, Yuval Tassa, Emilio Parisotto, Jan Humplik, Tuomas Haarnoja, Roland Hafner, Markus Wulfmeier, Michael Neunert, Ben Moran, Noah Y. Siegel, Andrea Huber, Francesco Romano, Nathan Batchelor, Federico Casarini, Josh Merel, Raia Hadsell, Nicolas Heess:
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors. CoRR abs/2203.17138 (2022) - [i102]Wenxuan Zhou, Steven Bohez, Jan Humplik, Abbas Abdolmaleki, Dushyant Rao, Markus Wulfmeier, Tuomas Haarnoja, Nicolas Heess:
Offline Distillation for Robot Lifelong Learning with Imbalanced Experience. CoRR abs/2204.05893 (2022) - [i101]Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez:
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. CoRR abs/2204.08957 (2022) - [i100]Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, Siqi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin A. Riedmiller:
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach. CoRR abs/2204.10256 (2022) - [i99]Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas:
A Generalist Agent. CoRR abs/2205.06175 (2022) - [i98]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio:
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel. CoRR abs/2205.10607 (2022) - [i97]Alexandre Galashov, Josh Merel, Nicolas Heess:
Data augmentation for efficient learning from parametric experts. CoRR abs/2205.11448 (2022) - [i96]Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess:
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. CoRR abs/2205.15879 (2022) - [i95]Sasha Salter, Markus Wulfmeier, Dhruva Tirumala, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell, Dushyant Rao:
MO2: Model-Based Offline Options. CoRR abs/2209.01947 (2022) - [i94]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio:
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2210.03022 (2022) - [i93]Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess:
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields. CoRR abs/2210.04932 (2022) - [i92]Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin A. Riedmiller:
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration. CoRR abs/2211.13743 (2022) - [i91]Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb:
Representation Learning in Deep RL via Discrete Information Bottleneck. CoRR abs/2212.13835 (2022) - 2021
- [j5]Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis:
Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 71: 41-88 (2021) - [c70]Sandy H. Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J. Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell:
A Constrained Multi-Objective Reinforcement Learning Framework. CoRL 2021: 883-893 - [c69]Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan, Noah Yamamoto Siegel, Saran Tunyasuvunakool, Federico Casarini, Nathan Batchelor, Francesco Romano, Stefano Saliceti, Martin A. Riedmiller, S. M. Ali Eslami, Nicolas Heess:
Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. CoRL 2021: 1502-1511 - [c68]Martin A. Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess:
Collect & Infer - a fresh look at data-efficient Reinforcement Learning. CoRL 2021: 1736-1744 - [c67]Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rémi Munos:
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML 2021: 7654-7664 - [c66]Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller:
Data-efficient Hindsight Off-policy Option Learning. ICML 2021: 11340-11350 - [c65]Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih:
Entropic Desired Dynamics for Intrinsic Control. NeurIPS 2021: 11436-11448 - [c64]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio:
Neural Production Systems. NeurIPS 2021: 25673-25687 - [i90]Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio:
Neural Production Systems. CoRR abs/2103.01937 (2021) - [i89]Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess:
From Motor Control to Team Play in Simulated Humanoid Football. CoRR abs/2105.12196 (2021) - [i88]Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra
, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvári, Raia Hadsell, Nicolas Heess, Martin A. Riedmiller:
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. CoRR abs/2106.08199 (2021) - [i87]Martin A. Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess:
Collect & Infer - a fresh look at data-efficient Reinforcement Learning. CoRR abs/2108.10273 (2021) - [i86]