| Motion planning networks AH Qureshi, A Simeonov, MJ Bency, MC Yip 2019 International Conference on Robotics and Automation (ICRA), 2118-2124, 2019 | 282 | 2019 |
| Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments AH Qureshi, Y Ayaz Robotics and Autonomous Systems 68, 1-11, 2015 | 236 | 2015 |
| Motion planning networks: Bridging the gap between learning-based and classical motion planners AH Qureshi, Y Miao, A Simeonov, MC Yip IEEE Transactions on Robotics 37 (1), 48-66, 2020 | 224 | 2020 |
| Potential functions based sampling heuristic for optimal path planning AH Qureshi, Y Ayaz Autonomous Robots 40, 1079-1093, 2016 | 209 | 2016 |
| Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments Z Tahir, AH Qureshi, Y Ayaz, R Nawaz Robotics and Autonomous Systems 108, 13-27, 2018 | 173 | 2018 |
| Robot gains social intelligence through multimodal deep reinforcement learning AH Qureshi, Y Nakamura, Y Yoshikawa, H Ishiguro 2016 IEEE-RAS 16th international conference on humanoid robots (humanoids …, 2016 | 135 | 2016 |
| Neural path planning: Fixed time, near-optimal path generation via oracle imitation MJ Bency, AH Qureshi, MC Yip 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 86 | 2019 |
| Adversarial Imitation via Variational Inverse Reinforcement Learning AH Qureshi, B Boots, MC Yip International Conference on Learning Representations (ICLR 2019), 2018 | 84 | 2018 |
| Deeply informed neural sampling for robot motion planning AH Qureshi, MC Yip 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 81 | 2018 |
| Intrinsically motivated reinforcement learning for human–robot interaction in the real-world AH Qureshi, Y Nakamura, Y Yoshikawa, H Ishiguro Neural Networks 107, 23-33, 2018 | 76 | 2018 |
| Nerp: Neural rearrangement planning for unknown objects AH Qureshi, A Mousavian, C Paxton, MC Yip, D Fox arXiv preprint arXiv:2106.01352, 2021 | 71 | 2021 |
| Potential guided directional-RRT* for accelerated motion planning in cluttered environments AH Qureshi, KF Iqbal, SM Qamar, F Islam, Y Ayaz, N Muhammad 2013 IEEE International Conference on Mechatronics and Automation, 519-524, 2013 | 58 | 2013 |
| MPC-MPNet: Model-predictive motion planning networks for fast, near-optimal planning under kinodynamic constraints L Li, Y Miao, AH Qureshi, MC Yip IEEE Robotics and Automation Letters 6 (3), 4496-4503, 2021 | 56 | 2021 |
| Show, attend and interact: Perceivable human-robot social interaction through neural attention Q-network AH Qureshi, Y Nakamura, Y Yoshikawa, H Ishiguro Robotics and Automation (ICRA), 2017 IEEE International Conference on, 1639 …, 2017 | 42 | 2017 |
| APGD-RRT*: Adaptive potential guided directional-RRT* AH Qureshi, S Mumtaz, KF Iqbal, B Ali, Y Ayaz, F Ahmed, MS Muhammad, ... Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on …, 2013 | 38* | 2013 |
| Composing Task-Agnostic Policies with Deep Reinforcement Learning AH Qureshi, JJ Johnson, Y Qin, B Boots, MC Yip International Conference on Learning Representations (ICLR 2020), 2019 | 37* | 2019 |
| Neural manipulation planning on constraint manifolds AH Qureshi, J Dong, A Choe, MC Yip IEEE Robotics and Automation Letters 5 (4), 6089-6096, 2020 | 34 | 2020 |
| Constrained motion planning networks x AH Qureshi, J Dong, A Baig, MC Yip IEEE Transactions on Robotics 38 (2), 868-886, 2021 | 32 | 2021 |
| Dynamically constrained motion planning networks for non-holonomic robots JJ Johnson, L Li, F Liu, AH Qureshi, MC Yip 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 31 | 2020 |
| Triangular geometry based optimal motion planning using RRT*-motion planner AH Qureshi, S Mumtaz, KF Iqbal, Y Ayaz, MS Muhammad, O Hasan, ... 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC), 380-385, 2014 | 24 | 2014 |
Michael C. YipUniversity of California at San Diego (UCSD)Vahvistettu sähköpostiosoite verkkotunnuksessa ucsd.edu
