| RAMBO-RL: Robust adversarial model-based offline reinforcement learning M Rigter, B Lacerda, N Hawes Advances in Neural Information Processing Systems (NeurIPS), 2022 | 106 | 2022 |
| Using energy requirements to compare the suitability of alternative methods for broadcast and site-specific weed control GRY Coleman, A Stead, MP Rigter, Z Xu, D Johnson, GM Brooker, ... Weed Technology, 2019 | 69 | 2019 |
| Risk-Averse Bayes-Adaptive Reinforcement Learning M Rigter, B Lacerda, N Hawes Advances in Neural Information Processing Systems (NeurIPS), 2021 | 42 | 2021 |
| Differential flatness transformations for aggressive quadrotor flight B Morrell, M Rigter, G Merewether, R Reid, R Thakker, T Tzanetos, ... IEEE International Conference on Robotics and Automation (ICRA), 2018 | 39 | 2018 |
| A Framework for Learning from Demonstration with Minimal Human Effort M Rigter, B Lacerda, N Hawes IEEE Robotics and Automation Letters (RA-L), 2020 | 32 | 2020 |
| Comparison of trajectory optimization algorithms for high-speed quadrotor flight near obstacles B Morrell, R Thakker, G Merewether, R Reid, M Rigter, T Tzanetos, ... IEEE Robotics and Automation Letters (RA-L), 2018 | 25 | 2018 |
| Minimax regret optimisation for robust planning in uncertain markov decision processes M Rigter, B Lacerda, N Hawes AAAI Conference on Artificial Intelligence (AAAI), 2021 | 14 | 2021 |
| Shared autonomy systems with stochastic operator models C Costen, M Rigter, B Lacerda, N Hawes International Joint Conference on Artificial Intelligence (IJCAI), 2022 | 12 | 2022 |
| Planning for Risk-Aversion and Expected Value in MDPs M Rigter, P Duckworth, B Lacerda, N Hawes ICAPS International Conference on Automated Planning and Scheduling (ICAPS), 2022 | 10 | 2022 |
| Risk-constrained planning for multi-agent systems with shared resources AL Gautier, M Rigter, B Lacerda, N Hawes, M Wooldridge International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023 | 7 | 2023 |
| One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning M Rigter, B Lacerda, N Hawes Advances in Neural Information Processing Systems (NeurIPS), 2023 | 7 | 2023 |
| An autonomous quadrotor system for robust high-speed flight through cluttered environments without GPS M Rigter, B Morrell, RG Reid, GB Merewether, T Tzanetos, V Rajur, ... International Conference on Intelligent Robots and Systems (IROS), 0 | 7* | |
| Planning with hidden parameter polynomial MDPs C Costen, M Rigter, B Lacerda, N Hawes AAAI Conference on Artificial Intelligence (AAAI), 2023 | 6 | 2023 |
| Robot path planning for multiple target regions S Ishida, M Rigter, N Hawes European Conference on Mobile Robots (ECMR), 2019 | 6 | 2019 |
| The Essential Role of Causality in Foundation World Models for Embodied AI T Gupta, W Gong, C Ma, N Pawlowski, A Hilmkil, M Scetbon, M Rigter, ... arXiv preprint arXiv:2402.06665, 2024 | 5 | 2024 |
| World models via policy-guided trajectory diffusion M Rigter, J Yamada, I Posner Transactions on Machine Learning Research (TMLR), 2024 | 4 | 2024 |
| Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction M Rigter, D Dervovic, P Hassanzadeh, J Long, P Zehtabi, D Magazzeni AAAI Conference on Artificial Intelligence (AAAI), 2022 | 4 | 2022 |
| Reward-Free Curricula for Training Robust World Models M Rigter, M Jiang, I Posner International Conference on Learning Representations (ICLR), 2024 | 2 | 2024 |
| TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer J Yamada, M Rigter, J Collins, I Posner IEEE International Conference on Robotics and Automation (ICRA), 2024 | | 2024 |
| Risk-Sensitive and Robust Model-Based Reinforcement Learning and Planning M Rigter PhD Thesis, University of Oxford, 2023 | | 2023 |
Nick HawesOxford Robotics Institute, University of Oxford在 robots.ox.ac.uk 的电子邮件经过验证
