| Habitat 2.0: Training home assistants to rearrange their habitat A Szot, A Clegg, E Undersander, E Wijmans, Y Zhao, J Turner, N Maestre, ... Neural Information Processing Systems (NeurIPS), 2021 | 284 | 2021 |
| Continuous-time Gaussian process motion planning via probabilistic inference M Mukadam, J Dong, X Yan, F Dellaert, B Boots International Journal of Robotics Research (IJRR), 2018 | 183 | 2018 |
| Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs J Dong, M Mukadam, F Dellaert, B Boots Robotics: Science and Systems (RSS), 2016 | 138 | 2016 |
| Gaussian Process Motion Planning M Mukadam, X Yan, B Boots International Conference on Robotics and Automation (ICRA), 2016 | 126 | 2016 |
| Tactical Decision Making for Lane Changing with Deep Reinforcement Learning M Mukadam, A Cosgun, A Nakhaei, K Fujimura NIPS 2017 Workshop on Machine Learning for Intelligent Transportation Systems, 2017 | 116 | 2017 |
| Where2act: From pixels to actions for articulated 3d objects K Mo, L Guibas, M Mukadam, A Gupta, S Tulsiani International Conference on Computer Vision (ICCV), 2021 | 89 | 2021 |
| Towards robust skill generalization: Unifying learning from demonstration and motion planning MA Rana, M Mukadam, SR Ahmadzadeh, S Chernova, B Boots Conference on Robot Learning (CoRL), 2017 | 86 | 2017 |
| iSDF: Real-Time Neural Signed Distance Fields for Robot Perception J Ortiz, A Clegg, J Dong, E Sucar, D Novotny, M Zollhoefer, M Mukadam Robotics: Science and Systems (RSS), 2022 | 70 | 2022 |
| RMPflow: A computational graph for automatic motion policy generation CA Cheng, M Mukadam, J Issac, S Birchfield, D Fox, B Boots, N Ratliff Algorithmic Foundations of Robotics (WAFR), 2018 | 68 | 2018 |
| Neural Dynamic Policies for End-to-End Sensorimotor Learning S Bahl, M Mukadam, A Gupta, D Pathak Neural Information Processing Systems (NeurIPS), 2020 | 61 | 2020 |
| Differentiable Gaussian process motion planning M Bhardwaj, B Boots, M Mukadam International Conference on Robotics and Automation (ICRA), 2020 | 51 | 2020 |
| Theseus: A library for differentiable nonlinear optimization L Pineda, T Fan, M Monge, S Venkataraman, P Sodhi, R Chen, J Ortiz, ... Neural Information Processing Systems (NeurIPS), 2022 | 49 | 2022 |
| No RL, No Simulation: Learning to Navigate without Navigating M Hahn, D Chaplot, S Tulsiani, M Mukadam, JM Rehg, A Gupta Neural Information Processing Systems (NeurIPS), 2021 | 46 | 2021 |
| Encoding Physical Constraints in Differentiable Newton-Euler Algorithm G Sutanto, A Wang, Y Lin, M Mukadam, G Sukhatme, A Rai, F Meier Learning for Dynamics and Control (L4DC), 2020 | 46 | 2020 |
| TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs C Agia, KM Jatavallabhula, M Khodeir, O Miksik, V Vineet, M Mukadam, ... Conference on Robot Learning (CoRL), 2021 | 33 | 2021 |
| Learning Tactile Models for Factor Graph-based Estimation P Sodhi, M Kaess, M Mukadam, S Anderson International Conference on Robotics and Automation (ICRA), 2021 | 32 | 2021 |
| Joint Inference of Kinematic and Force Trajectories with Visuo-Tactile Sensing A Lambert, M Mukadam, B Sundaralingam, N Ratliff, B Boots, D Fox International Conference on Robotics and Automation (ICRA), 2019 | 30 | 2019 |
| STEAP: simultaneous trajectory estimation and planning M Mukadam, J Dong, F Dellaert, B Boots Autonomous Robots (AuRo), 2018 | 29 | 2018 |
| Motion planning with graph-based trajectories and Gaussian process inference E Huang, M Mukadam, Z Liu, B Boots International Conference on Robotics and Automation (ICRA), 2017 | 27 | 2017 |
| Simultaneous Trajectory Estimation and Planning via Probabilistic Inference M Mukadam, J Dong, F Dellaert, B Boots Robotics: Science and Systems (RSS), 2017 | 25 | 2017 |
Byron BootsAssociate Professor, University of Washington在 cs.washington.edu 的电子邮件经过验证
