| LLM+P: Empowering Large Language Models with Optimal Planning Proficiency B Liu, Y Jiang, X Zhang, Q Liu, S Zhang, J Biswas, P Stone arXiv preprint arXiv:2304.11477, 2023 | 666 | 2023 |
| Conflict-averse gradient descent for multi-task learning B Liu, X Liu, X Jin, P Stone, Q Liu Advances in Neural Information Processing Systems 34, 18878-18890, 2021 | 577 | 2021 |
| Libero: Benchmarking knowledge transfer for lifelong robot learning B Liu, Y Zhu, C Gao, Y Feng, Q Liu, Y Zhu, P Stone Advances in Neural Information Processing Systems 36, 44776-44791, 2023 | 504 | 2023 |
| Motion planning and control for mobile robot navigation using machine learning: a survey X Xiao, B Liu, G Warnell, P Stone Autonomous Robots 46 (5), 569-597, 2022 | 376 | 2022 |
| The llama 4 herd: The beginning of a new era of natively multimodal ai innovation AI Meta https://ai. meta. com/blog/llama-4-multimodal-intelligence/, checked on 4 (7 …, 2025 | 255 | 2025 |
| Continual Learning and Private Unlearning B Liu, Q Liu, P Stone Proceedings of The 1st Conference on Lifelong Learning Agents, PMLR 199:243 …, 2022 | 178 | 2022 |
| BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach B Liu, M Ye, S Wright, P Stone Advances in Neural Information Processing Systems, 2022 | 152 | 2022 |
| A lifelong learning approach to mobile robot navigation B Liu, X Xiao, P Stone IEEE Robotics and Automation Letters 6 (2), 1090-1096, 2021 | 151 | 2021 |
| Appld: Adaptive planner parameter learning from demonstration X Xiao, B Liu, G Warnell, J Fink, P Stone IEEE Robotics and Automation Letters 5 (3), 4541-4547, 2020 | 104 | 2020 |
| Famo: Fast adaptive multitask optimization B Liu, Y Feng, P Stone, Q Liu Advances in Neural Information Processing Systems 36, 57226-57243, 2023 | 90 | 2023 |
| Human gaze assisted artificial intelligence: A review R Zhang, A Saran, B Liu, Y Zhu, S Guo, S Niekum, D Ballard, M Hayhoe IJCAI: Proceedings of the Conference 2020, 4951, 2020 | 89 | 2020 |
| Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition B Liu, Q Liu, P Stone, A Garg, Y Zhu, A Anandkumar International Conference on Machine Learning 2021, 2021 | 83 | 2021 |
| Appl: Adaptive planner parameter learning X Xiao, Z Wang, Z Xu, B Liu, G Warnell, G Dhamankar, A Nair, P Stone Robotics and Autonomous Systems 154, 104132, 2022 | 78 | 2022 |
| Benchmarking reinforcement learning techniques for autonomous navigation Z Xu, B Liu, X Xiao, A Nair, P Stone arXiv preprint arXiv:2210.04839, 2022 | 75 | 2022 |
| Applr: Adaptive planner parameter learning from reinforcement Z Xu, G Dhamankar, A Nair, X Xiao, G Warnell, B Liu, Z Wang, P Stone 2021 IEEE international conference on robotics and automation (ICRA), 6086-6092, 2021 | 75 | 2021 |
| Firefly neural architecture descent: a general approach for growing neural networks L Wu, B Liu, P Stone, Q Liu Advances in Neural Information Processing Systems 33, 2021 | 75 | 2021 |
| Toward agile maneuvers in highly constrained spaces: Learning from hallucination X Xiao, B Liu, G Warnell, P Stone IEEE Robotics and Automation Letters 6 (2), 1503-1510, 2021 | 69 | 2021 |
| Appli: Adaptive planner parameter learning from interventions Z Wang, X Xiao, B Liu, G Warnell, P Stone 2021 IEEE international conference on robotics and automation (ICRA), 6079-6085, 2021 | 68 | 2021 |
| Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings K Nweye, B Liu, P Stone, Z Nagy Energy and AI 10, 100202, 2022 | 62 | 2022 |
| Machine versus human attention in deep reinforcement learning tasks SS Guo, R Zhang, B Liu, Y Zhu, D Ballard, M Hayhoe, P Stone Advances in neural information processing systems 34, 25370-25385, 2021 | 54* | 2021 |
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
