Leonard Hasenclever

According to our database, Leonard Hasenclever authored at least 32 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning to Learn Faster from Human Feedback with Language Model Predictive Control.

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

CoRR, 2024

2023

Replay across Experiments: A Natural Extension of Off-Policy RL.

Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier

CoRR, 2023

Towards A Unified Agent with Foundation Models.

Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin A. Riedmiller

CoRR, 2023

A Generalist Dynamics Model for Control.

Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin A. Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess

CoRR, 2023

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning.

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

CoRR, 2023

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains.

Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin A. Riedmiller

CoRR, 2023

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields.

Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Language to Rewards for Robotic Skill Synthesis.

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

Proceedings of the Conference on Robot Learning, 2023

2022

From motor control to team play in simulated humanoid football.

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

Sci. Robotics, 2022

Behavior Priors for Efficient Reinforcement Learning.

Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

J. Mach. Learn. Res., 2022

Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors.

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

CoRR, 2022

Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner.

Philémon Brakel, Steven Bohez, Leonard Hasenclever, Nicolas Heess, Konstantinos Bousmalis

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Learning transferable motor skills with hierarchical latent mixture policies.

Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

Proceedings of the Tenth International Conference on Learning Representations, 2022

Evaluating Model-Based Planning and Planner Amortization for Continuous Control.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Learning Dynamics Models for Model Predictive Agents.

Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, Yuval Tassa

CoRR, 2021

2020

Catch & Carry: reusable neural controllers for vision-guided whole-body tasks.

Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess

ACM Trans. Graph., 2020

Importance Weighted Policy Learning and Adaption.

Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess

CoRR, 2020

Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning.

Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber

CoRR, 2020

CoMic: Complementary Task Learning & Mimicry for Reusable Skills.

Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel

Proceedings of the 37th International Conference on Machine Learning, 2020

A distributional view on multi-objective policy optimization.

Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Reusable neural skill embeddings for vision-guided whole body movement and object manipulation.

Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess

CoRR, 2019

Meta reinforcement learning as task inference.

Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess

CoRR, 2019

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL.

Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess

CoRR, 2019

Neural Probabilistic Motor Primitives for Humanoid Control.

Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

Proceedings of the 7th International Conference on Learning Representations, 2019

Information asymmetry in KL-regularized RL.

Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

Proceedings of the 7th International Conference on Learning Representations, 2019

Observational Learning by Reinforcement Learning.

Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Probabilistic machine learning: methods and applications to continuous control.

Leonard Hasenclever

PhD thesis, 2018

Sylvester Normalizing Flows for Variational Inference.

Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Mix & Match Agent Curricula for Reinforcement Learning.

Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.

Leonard Hasenclever, Stefan Webb, Thibaut Liénart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

J. Mach. Learn. Res., 2017

Relativistic Monte Carlo.

Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2014

An investigation into irreducible autocatalytic sets and power law distributed catalysis.

Wim Hordijk, Leonard Hasenclever, Jie Gao, Dilyana Mincheva, Jotun Hein

Nat. Comput., 2014