Controller Design for Quadrotor UAVs using Reinforcement Learning. - Haitham Bou-Ammar; Holger Voos; Wolfgang Ertel (CCA, 2010)

Nonlinear Tracking and Landing Controller for Quadrotor Aerial Robots. - Holger Voos; Haitham Bou-Ammar (CCA, 2010)

Reinforcement Learning Transfer via Common Subspaces. - Haitham Bou-Ammar; Matthew E. Taylor (ALA, 2011)

Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping. - Haitham Bou-Ammar; Matthew E. Taylor; Karl Tuyls; Gerhard Weiss 001 (EUMAS, 2011)

Reinforcement learning transfer via sparse coding. - Haitham Bou-Ammar; Karl Tuyls; Matthew E. Taylor; Kurt Driessens; Gerhard Weiss 001 (AAMAS, 2012)

Evolutionary Dynamics of Ant Colony Optimization. - Haitham Bou-Ammar; Karl Tuyls; Michael Kaisers (MATES, 2012)

Optimizing complex automated negotiation using sparse pseudo-input gaussian processes. - Siqi Chen 001; Haitham Bou-Ammar; Karl Tuyls; Gerhard Weiss 001 (AAMAS, 2013)

Swarm-based evaluation of nonparametric SysML mechatronics system design. - Mohammad Chami; Haitham Bou-Ammar; Holger Voos; Karl Tuyls; Gerhard Weiss 001 (ICM, 2013)

Conformity-Based Transfer AdaBoost Algorithm. - Shuang Zhou 001; Evgueni N. Smirnov; Haitham Bou-Ammar; Ralf Peeters (AIAI, 2013)

Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains. - Siqi Chen 001; Haitham Bou-Ammar; Karl Tuyls; Gerhard Weiss 001 (IJCAI, 2013)

Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines. - Haitham Bou-Ammar; Decebal Constantin Mocanu; Matthew E. Taylor; Kurt Driessens; Karl Tuyls; Gerhard Weiss 001 (ECML/PKDD (2), 2013)

Theory of Cooperation in Complex Social Networks. - Bijan Ranjbar Sahraei; Haitham Bou-Ammar; Daan Bloembergen; Karl Tuyls; Gerhard Weiss 001 (AAAI, 2014)

Online Multi-Task Gradient Temporal-Difference Learning. - Vishnu Purushothaman Sreenivasan; Haitham Bou-Ammar; Eric Eaton (AAAI, 2014)

Effects of Evolution on the Emergence of Scale Free Networks. - Bijan Ranjbar Sahraei; Dean Bloembergen; Haitham Ammar; Karl Tuyls; Gerhard Weiss 001 (ALIFE, 2014)

Evolution of cooperation in arbitrary complex networks. - Bijan Ranjbar Sahraei; Haitham Bou-Ammar; Daan Bloembergen; Karl Tuyls; Gerhard Weiss 001 (AAMAS, 2014)

Influencing Social Networks: An Optimal Control Study. - Daan Bloembergen; Bijan Ranjbar Sahraei; Haitham Bou-Ammar; Karl Tuyls; Gerhard Weiss 001 (ECAI, 2014)

Online Multi-Task Learning for Policy Gradient Methods. - Haitham Bou-Ammar; Eric Eaton; Paul Ruvolo; Matthew E. Taylor (ICML, 2014)

Inexpensive user tracking using Boltzmann Machines. - Elena Mocanu; Decebal Constantin Mocanu; Haitham Bou-Ammar; Zoran Zivkovic; Antonio Liotta; Evgueni N. Smirnov (SMC, 2014)

On the Degree Distribution of Pólya Urn Graph Processes. - Rasul Tutunov; Haitham Bou-Ammar; Ali Jadbabaie; Eric Eaton (CoRR, 2014)

Automated Transfer for Reinforcement Learning Tasks. - Haitham Bou-Ammar; Siqi Chen 001; Karl Tuyls; Gerhard Weiss 001 (Künstliche Intell., 2014)

Transfer for Automated Negotiation. - Siqi Chen 001; Haitham Bou-Ammar; Karl Tuyls; Gerhard Weiss 001 (Künstliche Intell., 2014)

Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment. - Haitham Bou-Ammar; Eric Eaton; Paul Ruvolo; Matthew E. Taylor (AAAI, 2015)

On the Skewed Degree Distribution of Hierarchical Networks. - Bijan Ranjbar Sahraei; Haitham Bou-Ammar; Karl Tuyls; Gerhard Weiss 001 (ASONAM, 2015)

Fast, accurate second order methods for network optimization. - Rasul Tutunov; Haitham Bou-Ammar; Ali Jadbabaie (CDC, 2015)

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. - Haitham Bou-Ammar; Rasul Tutunov; Eric Eaton (ICML, 2015)

Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning. - Haitham Bou-Ammar; Eric Eaton; José-Marcio Luna; Paul Ruvolo (IJCAI, 2015)

Reduced reference image quality assessment via Boltzmann Machines. - Decebal Constantin Mocanu; Georgios Exarchakos; Haitham Bou-Ammar; Antonio Liotta (IM, 2015)

A Fast Distributed Solver for Symmetric Diagonally Dominant Linear Equations. - Rasul Tutunov; Haitham Bou-Ammar; Ali Jadbabaie (CoRR, 2015)

Factored four way conditional restricted Boltzmann machines for activity recognition. - Decebal Constantin Mocanu; Haitham Bou-Ammar; Dietwig Lowet; Kurt Driessens; Antonio Liotta; Gerhard Weiss 001; Karl Tuyls (Pattern Recognit. Lett., 2015)

An exact distributed newton method for reinforcement learning. - Rasul Tutunov; Haitham Bou-Ammar; Ali Jadbabaie (CDC, 2016)

Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer. - Yusen Zhan; Haitham Bou-Ammar; Matthew E. Taylor (IJCAI, 2016)

On the prevalence of hierarchies in social networks. - Bijan Ranjbar Sahraei; Haitham Bou-Ammar; Karl Tuyls; Gerhard Weiss 001 (Soc. Netw. Anal. Min., 2016)

Scalable Multitask Policy Gradient Reinforcement Learning. - Salam El Bsat; Haitham Bou-Ammar; Matthew E. Taylor (AAAI, 2017)

Distributed lifelong reinforcement learning with sub-linear regret. - Rasul Tutunov; Julia El Zini; Haitham Bou-Ammar; Ali Jadbabaie (CDC, 2017)

Correctness-by-Learning of Infinite-State Component-Based Systems. - Haitham Bou-Ammar; Mohamad Jaber 001; Mohamed Nassar 001 (FACS, 2017)

An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. - Felix Leibfried; Jordi Grau-Moya; Haitham Bou-Ammar (CoRR, 2017)

Nonconvex Policy Search Using Variational Inequalities. - Yusen Zhan; Haitham Bou-Ammar; Matthew E. Taylor (Neural Comput., 2017)

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. - Decebal Constantin Mocanu; Haitham Bou-Ammar; Luis Puig; Eric Eaton; Antonio Liotta (Pattern Recognit., 2017)

Scalable lifelong reinforcement learning. - Yusen Zhan; Haitham Bou-Ammar; Matthew E. Taylor (Pattern Recognit., 2017)

Balancing Two-Player Stochastic Games with Soft Q-Learning. - Jordi Grau-Moya; Felix Leibfried; Haitham Bou-Ammar (IJCAI, 2018)

Distributed Multitask Reinforcement Learning with Quadratic Convergence. - Rasul Tutunov; Dongho Kim; Haitham Bou-Ammar (NeurIPS, 2018)

Reports on the 2018 AAAI Spring Symposium Series. - Christopher Amato; Haitham Bou-Ammar; Elizabeth F. Churchill; Erez Karpas; Takashi Kido; Mike Kuniavsky; William F. Lawless; Francesca Rossi 001; Frans A. Oliehoek; Stephen Russell 001; Keiki Takadama; Siddharth Srivastava 001; et al. (AI Mag., 2018)

Learning High-level Representations from Demonstrations. - Garrett Andersen; Peter Vrancx; Haitham Bou-Ammar (CoRR, 2018)

Model-Based Stabilisation of Deep Reinforcement Learning. - Felix Leibfried; Rasul Tutunov; Peter Vrancx; Haitham Bou-Ammar (CoRR, 2018)

Multi-View Reinforcement Learning. - Minne Li; Lisheng Wu; Jun Wang 012; Haitham Bou-Ammar (NeurIPS, 2019)

Wasserstein Robust Reinforcement Learning. - Mohammed Amin Abdullah 001; Hang Ren; Haitham Bou-Ammar; Vladimir Milenkovic; Rui Luo 001; Mingtian Zhang; Jun Wang 012 (CoRR, 2019)

Distributed Newton Method for Large-Scale Consensus Optimization. - Rasul Tutunov; Haitham Bou-Ammar; Ali Jadbabaie (IEEE Trans. Autom. Control., 2019)

Learning to Communicate Implicitly by Actions. - Zheng Tian 002; Shihao Zou; Ian Davies; Tim Warr; Lisheng Wu; Haitham Bou-Ammar; Jun Wang 012 (AAAI, 2020)

Derivative-Free & Order-Robust Optimisation. - Haitham Ammar; Victor Gabillon; Rasul Tutunov; Michal Valko (AISTATS, 2020)

αα-Rank: Practically Scaling α-Rank through Stochastic Optimisation. - Yaodong Yang 001; Rasul Tutunov; Phu Sakulwongtana; Haitham Bou-Ammar (AAMAS, 2020)

SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving. - Ming Zhou 006; Jun Luo 009; Julian Villela; Yaodong Yang 002; David Rusu; Jiayu Miao; Weinan Zhang 001; Montgomery Alban; Iman Fadakar; Zheng Chen; Chongxi Huang; Ying Wen 001; et al. (CoRL, 2020)

Compositional ADAM: An Adaptive Compositional Solver. - Rasul Tutunov; Minne Li; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2020)

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. - Ming Zhou 006; Jun Luo 009; Julian Villela; Yaodong Yang 002; David Rusu; Jiayu Miao; Weinan Zhang 001; Montgomery Alban; Iman Fadakar; Zheng Chen; Aurora Chongxi Huang; Ying Wen 001; et al. (CoRR, 2020)

HEBO: Heteroscedastic Evolutionary Bayesian Optimisation. - Alexander I. Cowen-Rivers; Wenlong Lyu; Zhi Wang 001; Rasul Tutunov; Jianye Hao; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2020)

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. - Yaodong Yang 002; Jun Luo 009; Ying Wen 001; Oliver Slumbers; Daniel Graves; Haitham Bou-Ammar; Jun Wang 012; Matthew E. Taylor (AAMAS, 2021)

Robot Reinforcement Learning on the Constraint Manifold. - Puze Liu; Davide Tateo; Haitham Bou-Ammar; Jan Peters 001 (CoRL, 2021)

Efficient and Reactive Planning for High Speed Robot Air Hockey. - Puze Liu; Davide Tateo; Haitham Bou-Ammar; Jan Peters 001 (IROS, 2021)

Efficient Semi-Implicit Variational Inference. - Vincent Moens; Hang Ren; Alexandre Maraval; Rasul Tutunov; Jun Wang 012; Haitham Ammar (CoRR, 2021)

Online Double Oracle. - Le Cong Dinh; Yaodong Yang 001; Zheng Tian 002; Nicolas Perez Nieves; Oliver Slumbers; David Henry Mguni; Haitham Bou-Ammar; Jun Wang 012 (CoRR, 2021)

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning. - Antoine Grosnit; Rasul Tutunov; Alexandre Max Maraval; Ryan-Rhys Griffiths; Alexander I. Cowen-Rivers; Lin Yang; Lin Zhu; Wenlong Lyu; Zhitang Chen; Jun Wang 012; Jan Peters 001; Haitham Bou-Ammar (CoRR, 2021)

Implicit Variational Conditional Sampling with Normalizing Flows. - Vincent Moens; Aivar Sootla; Haitham Bou-Ammar; Jun Wang 012 (CoRR, 2021)

Are We Forgetting about Compositional Optimisers in Bayesian Optimisation? - Antoine Grosnit; Alexander I. Cowen-Rivers; Rasul Tutunov; Ryan-Rhys Griffiths; Jun Wang 012; Haitham Bou-Ammar (J. Mach. Learn. Res., 2021)

BOiLS: Bayesian Optimisation for Logic Synthesis. - Antoine Grosnit; Cédric Malherbe; Rasul Tutunov; Xingchen Wan; Jun Wang 012; Haitham Bou-Ammar (DATE, 2022)

Reinforcement Learning in Presence of Discrete Markovian Context Evolution. - Hang Ren; Aivar Sootla; Taher Jafferjee; Junxiao Shen; Jun Wang 012; Haitham Bou-Ammar (ICLR, 2022)

Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation. - Aivar Sootla; Alexander I. Cowen-Rivers; Taher Jafferjee; Ziyan Wang; David Henry Mguni; Jun Wang 012; Haitham Ammar (ICML, 2022)

Optimistic Tree Searches for Combinatorial Black-Box Optimization. - Cédric Malherbe; Antoine Grosnit; Rasul Tutunov; Haitham Bou-Ammar; Jun Wang 012 (NeurIPS, 2022)

Enhancing Safe Exploration Using Safety State Augmentation. - Aivar Sootla; Alexander I. Cowen-Rivers; Jun Wang 012; Haitham Bou-Ammar (NeurIPS, 2022)

Learning Geometric Constraints in Task and Motion Planning. - Tianyu Ren; Alexander Imani Cowen-Rivers; Haitham Bou-Ammar; Jan Peters 001 (CoRR, 2022)

AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation. - Mohammad Asif Khan 001; Alexander I. Cowen-Rivers; Derrick-Goh-Xin Deik; Antoine Grosnit; Kamil Dreczkowski; Philippe A. Robert; Victor Greiff; Rasul Tutunov; Dany Bou-Ammar; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2022)

Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations. - Xihan Li 001; Xiang Chen; Rasul Tutunov; Haitham Bou-Ammar; Lei Wang 198; Jun Wang 012 (CoRR, 2022)

Sample-Efficient Optimisation with Probabilistic Transformer Surrogates. - Alexandre Maraval; Matthieu Zimmer; Antoine Grosnit; Rasul Tutunov; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2022)

Structured Q-learning For Antibody Design. - Alexander I. Cowen-Rivers; Philip John Gorinski; Aivar Sootla; Asif Khan 008; Furui Liu; Jun Wang 012; Jan Peters 001; Haitham Bou-Ammar (CoRR, 2022)

HEBO: An Empirical Study of Assumptions in Bayesian Optimisation. - Alexander I. Cowen-Rivers; Wenlong Lyu; Rasul Tutunov; Zhi Wang 001; Antoine Grosnit; Ryan-Rhys Griffiths; Alexandre Max Maraval; Jianye Hao; Jun Wang 012; Jan Peters 001; Haitham Bou-Ammar (J. Artif. Intell. Res., 2022)

SAMBA: safe model-based & active reinforcement learning. - Alexander I. Cowen-Rivers; Daniel Palenicek; Vincent Moens; Mohammed Amin Abdullah 001; Aivar Sootla; Jun Wang 012; Haitham Bou-Ammar (Mach. Learn., 2022)

Online Double Oracle. - Le Cong Dinh; Stephen Marcus McAleer; Zheng Tian 002; Nicolas Perez Nieves; Oliver Slumbers; David Henry Mguni; Jun Wang 012; Haitham Bou-Ammar; Yaodong Yang 001 (Trans. Mach. Learn. Res., 2022)

Lightweight Structural Choices Operator for Technology Mapping. - Antoine Grosnit; Matthieu Zimmer; Rasul Tutunov; Xing Li; Lei Chen 031; Fan Yang; Mingxuan Yuan; Haitham Bou-Ammar (DAC, 2023)

Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? - Juliusz Krysztof Ziomek; Haitham Bou-Ammar (ICML, 2023)

Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions. - Desong Du; Shaohang Han; Naiming Qi; Haitham Bou-Ammar; Jun Wang 012; Wei Pan 004 (ICRA, 2023)

Online PCA in Converging Self-consistent Field Equations. - Xihan Li 001; Xiang Chen; Rasul Tutunov; Haitham Bou-Ammar; Lei Wang 198; Jun Wang 012 (NeurIPS, 2023)

Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. - Kamil Dreczkowski; Antoine Grosnit; Haitham Bou-Ammar (NeurIPS, 2023)

End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. - Alexandre Maraval; Matthieu Zimmer; Antoine Grosnit; Haitham Bou-Ammar (NeurIPS, 2023)

Contextual Causal Bayesian Optimisation. - Vahan Arsenyan; Antoine Grosnit; Haitham Bou-Ammar (CoRR, 2023)

Why Can Large Language Models Generate Correct Chain-of-Thoughts? - Rasul Tutunov; Antoine Grosnit; Juliusz Ziomek; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2023)

Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning. - Filippos Christianos; Georgios Papoudakis; Matthieu Zimmer; Thomas Coste; Zhihao Wu; Jingxuan Chen; Khyati Khandelwal; James Doran; Xidong Feng; Jiacheng Liu; Zheng Xiong; Yicheng Luo; et al. (CoRR, 2023)

Measures of diversity and space-filling designs for categorical data. - Cédric Malherbe; Emilio Domínguez-Sánchez; Merwan Barlier; Igor Colin; Haitham Bou-Ammar; Tom Diethe (ICML, 2024)

A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics. - Puze Liu; Jonas Günster; Niklas Funk; Simon Gröger; Dong Chen; Haitham Bou-Ammar; Julius Jankowski; Ante Maric; Sylvain Calinon; Andrej Orsula; Miguel S. Olivares-Méndez; Hongyi Zhou; et al. (NeurIPS, 2024)

Group Robust Preference Optimization in Reward-free RLHF. - Shyam Sundhar Ramesh; Yifan Hu; Iason Chaimalas; Viraj Mehta; Pier Giuseppe Sessa; Haitham Bou-Ammar; Ilija Bogunovic (NeurIPS, 2024)

Bayesian Reward Models for LLM Alignment. - Adam X. Yang; Maxime Robeyns; Thomas Coste; Jun Wang 012; Haitham Bou-Ammar; Laurence Aitchison (CoRR, 2024)

ZSL-RPPO: Zero-Shot Learning for Quadrupedal Locomotion in Challenging Terrains using Recurrent Proximal Policy Optimization. - Yao Zhao; Tao Wu; Yijie Zhu; Xiang Lu; Jun Wang 012; Haitham Bou-Ammar; Xinyu Zhang; Peng Du (CoRR, 2024)

ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning. - Christopher E. Mower; Yuhui Wan; Hongzhan Yu; Antoine Grosnit; Jonas Gonzalez-Billandon; Matthieu Zimmer; Jinlong Wang; Xinyu Zhang; Yao Zhao; Anbang Zhai; Puze Liu; Davide Tateo; et al. (CoRR, 2024)

Human-like Episodic Memory for Infinite Context LLMs. - Zafeirios Fountas; Martin Benfeghoul; Adnan Oomerjee; Fenia Christopoulou; Gerasimos Lampouras; Haitham Bou-Ammar; Jun Wang 012 (CoRR, 2024)

ShortCircuit: AlphaZero-Driven Circuit Design. - Dimitrios Tsaras; Antoine Grosnit; Lei Chen 031; Zhiyao Xie; Haitham Bou-Ammar; Mingxuan Yuan (CoRR, 2024)

SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks. - Fenia Christopoulou; Ronald Cardenas; Gerasimos Lampouras; Haitham Bou-Ammar; Jun Wang 012 (CoRR, 2024)

Efficient Reinforcement Learning with Large Language Model Priors. - Xue Yan; Yan Song 003; Xidong Feng; Mengyue Yang; Haifeng Zhang 002; Haitham Bou-Ammar; Jun Wang 012 (CoRR, 2024)

Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level. - Antoine Grosnit; Alexandre Maraval; James Doran; Giuseppe Paolo; Albert Thomas 001; Refinath Shahul Hameed Nabeezath Beevi; Jonas Gonzalez; Khyati Khandelwal; Ignacio Iacobacci; Abdelhakim Benechehab; Hamza Cherkaoui; Youssef Attia El Hili; et al. (CoRR, 2024)

Fast Kinodynamic Planning on the Constraint Manifold With Deep Neural Networks. - Piotr Kicki; Puze Liu; Davide Tateo; Haitham Bou-Ammar; Krzysztof Walas; Piotr Skrzypczynski; Jan Peters 001 (IEEE Trans. Robotics, 2024)

Human-inspired Episodic Memory for Infinite Context LLMs. - Zafeirios Fountas; Martin Benfeghoul; Adnan Oomerjee; Fenia Christopoulou; Gerasimos Lampouras; Haitham Bou-Ammar; Jun Wang 012 (ICLR, 2025)

Efficient Reinforcement Learning with Large Language Model Priors. - Xue Yan; Yan Song; Xidong Feng; Mengyue Yang; Haifeng Zhang; Haitham Bou-Ammar; Jun Wang (ICLR, 2025)

Mixture of Attentions For Speculative Decoding. - Matthieu Zimmer; Milan Gritta; Gerasimos Lampouras; Haitham Bou-Ammar; Jun Wang 012 (ICLR, 2025)

Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications. - Puze Liu; Haitham Bou-Ammar; Jan Peters 001; Davide Tateo (IEEE Trans. Robotics, 2025)

Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information. - Rasul Tutunov; Antoine Grosnit; Haitham Bou-Ammar (CoRR, 2025)

Almost Surely Safe Alignment of Large Language Models at Inference-Time. - Xiaotong Ji; Shyam Sundhar Ramesh; Matthieu Zimmer; Ilija Bogunovic; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2025)

Al-Khwarizmi: Discovering Physical Laws with Foundation Models. - Christopher E. Mower; Haitham Bou-Ammar (CoRR, 2025)

Bottlenecked Transformers: Periodic KV Cache Abstraction for Generalised Reasoning. - Adnan Oomerjee; Zafeirios Fountas; Zhongwei Yu; Haitham Bou-Ammar; Jun Wang 012 (CoRR, 2025)

Ark: An Open-source Python-based Framework for Robot Learning. - Magnus Dierking; Christopher E. Mower; Sarthak Das; Helong Huang; Jiacheng Qiu; Cody Reading; Wei Chen; Huidong Liang; Guowei Huang; Jan Peters 001; Xingyue Quan; Jun Wang 012; et al. (CoRR, 2025)

Bourbaki: Self-Generated and Goal-Conditioned MDPs for Theorem Proving. - Matthieu Zimmer; Xiaotong Ji; Rasul Tutunov; Anthony Bordg; Jun Wang 012; Haitham Bou-Ammar (CoRR, 2025)

Experience is the Best Teacher: Grounding VLMs for Robotics through Self-Generated Memory. - Guowei Lan; Kaixian Qu; René Zurbrügg; Changan Chen; Christopher E. Mower; Haitham Bou-Ammar; Marco Hutter 001 (CoRR, 2025)