An investigation into irreducible autocatalytic sets and power law distributed catalysis. - Wim Hordijk; Leonard Hasenclever; Jie Gao; Dilyana Mincheva; Jotun Hein (Nat. Comput., 2014)

Relativistic Monte Carlo. - Xiaoyu Lu; Valerio Perrone; Leonard Hasenclever; Yee Whye Teh; Sebastian J. Vollmer (AISTATS, 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)

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 (ICML, 2018)

Sylvester Normalizing Flows for Variational Inference. - Rianne van den Berg; Leonard Hasenclever; Jakub M. Tomczak; Max Welling (UAI, 2018)

Probabilistic machine learning: methods and applications to continuous control. - Leonard Hasenclever (2018)

Observational Learning by Reinforcement Learning. - Diana Borsa; Nicolas Heess; Bilal Piot; Siqi Liu 002; Leonard Hasenclever; Rémi Munos; Olivier Pietquin (AAMAS, 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 (ICLR (Poster), 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 (ICLR (Poster), 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)

Meta reinforcement learning as task inference. - Jan Humplik; Alexandre Galashov; Leonard Hasenclever; Pedro A. Ortega; Yee Whye Teh; Nicolas Heess (CoRR, 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)

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 (ICML, 2020)

CoMic: Complementary Task Learning & Mimicry for Reusable Skills. - Leonard Hasenclever; Fabio Pardo; Raia Hadsell; Nicolas Heess; Josh Merel (ICML, 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)

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)

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)

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)

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 (ICLR, 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 (ICLR, 2022)

Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. - Philémon Brakel; Steven Bohez; Leonard Hasenclever; Nicolas Heess; Konstantinos Bousmalis (IROS, 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; et al. (CoRR, 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)

From motor control to team play in simulated humanoid football. - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech M. Czarnecki; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Sci. Robotics, 2022)

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech Czarnecki 001; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Zenodo, 2022)

Language to Rewards for Robotic Skill Synthesis. - Wenhao Yu 003; 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; et al. (CoRL, 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 (ICRA, 2023)

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains. - Jingwei Zhang 001; Jost Tobias Springenberg; Arunkumar Byravan; Leonard Hasenclever; Abbas Abdolmaleki; Dushyant Rao; Nicolas Heess; Martin A. Riedmiller (CoRR, 2023)

A Generalist Dynamics Model for Control. - Ingmar Schubert; Jingwei Zhang 001; Jake Bruce; Sarah Bechtle; Emilio Parisotto; Martin A. Riedmiller; Jost Tobias Springenberg; Arunkumar Byravan; Leonard Hasenclever; Nicolas Heess (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)

Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning. - Norman Di Palo; Leonard Hasenclever; Jan Humplik; Arunkumar Byravan (CoLLAs, 2024)

Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. - 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; et al. (CoRL, 2024)

Replay across Experiments: A Natural Extension of Off-Policy RL. - 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 (ICLR, 2024)

Learning to Learn Faster from Human Feedback with Language Model Predictive Control. - Jacky Liang; Fei Xia 002; Wenhao Yu 003; Andy Zeng 001; Maria Attarian; Maria Bauzá Villalonga; Matthew Bennice; Alex Bewley; Adil Dostmohamed; Chuyuan Fu; Nimrod Gileadi; Marissa Giustina; et al. (Robotics: Science and Systems, 2024)

Learning to Learn Faster from Human Feedback with Language Model Predictive Control. - Jacky Liang; Fei Xia 002; Wenhao Yu 003; Andy Zeng 001; Montserrat Gonzalez Arenas; Maria Attarian; Maria Bauzá 001; Matthew Bennice; Alex Bewley; Adil Dostmohamed; Chuyuan Kelly Fu; Nimrod Gileadi; et al. (CoRR, 2024)

Learning agile soccer skills for a bipedal robot with deep reinforcement learning. - 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; et al. (Sci. Robotics, 2024)

Data Release for: Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. - 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; et al. (Zenodo, 2024)

Proc4Gem: Foundation models for physical agency through procedural generation. - Yixin Lin; Jan Humplik; Sandy H. Huang; Leonard Hasenclever; Francesco Romano; Stefano Saliceti; Daniel Zheng; José Enrique Chen; Catarina Barros; Adrian Collister; Matt Young; Adil Dostmohamed; et al. (CoRR, 2025)

Gemini Robotics: Bringing AI into the Physical World. - Gemini Robotics Team; Saminda Abeyruwan; Joshua Ainslie; Jean-Baptiste Alayrac; Montserrat Gonzalez Arenas; Travis Armstrong; Ashwin Balakrishna; Robert Baruch; Maria Bauzá 001; Michiel Blokzijl; Steven Bohez; Konstantinos Bousmalis; et al. (CoRR, 2025)

Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data. - Ben Moran; Mauro Comi; Arunkumar Byravan; Steven Bohez; Tom Erez; Zhibin Li; Leonard Hasenclever (CoRR, 2025)

Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model. - Vittorio La Barbera; Steven Bohez; Leonard Hasenclever; Yuval Tassa; John R. Hutchinson (CoRR, 2025)