| 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 | 496 | 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 | 229 | 2018 |
| 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 | 171 | 2021 |
| Gaussian Process Motion Planning M Mukadam, X Yan, B Boots International Conference on Robotics and Automation (ICRA), 2016 | 162 | 2016 |
| 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 | 160 | 2016 |
| 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 | 138 | 2022 |
| 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 | 126 | 2017 |
| 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 | 98 | 2017 |
| 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 | 93 | 2018 |
| Neural Dynamic Policies for End-to-End Sensorimotor Learning S Bahl, M Mukadam, A Gupta, D Pathak Neural Information Processing Systems (NeurIPS), 2020 | 90 | 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 | 85 | 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 | 80 | 2021 |
| Differentiable Gaussian process motion planning M Bhardwaj, B Boots, M Mukadam International Conference on Robotics and Automation (ICRA), 2020 | 69 | 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 | 68 | 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 | 61 | 2020 |
| Learning Tactile Models for Factor Graph-based Estimation P Sodhi, M Kaess, M Mukadam, S Anderson International Conference on Robotics and Automation (ICRA), 2021 | 43 | 2021 |
| Neural Grasp Distance Fields for Robot Manipulation T Weng, D Held, F Meier, M Mukadam International Conference on Robotics and Automation (ICRA), 2023 | 41 | 2023 |
| RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies CA Cheng, M Mukadam, J Issac, S Birchfield, D Fox, B Boots, N Ratliff Transactions on Automation Science and Engineering (T-ASE), 2020 | 39 | 2020 |
| STEAP: simultaneous trajectory estimation and planning M Mukadam, J Dong, F Dellaert, B Boots Autonomous Robots (AuRo), 2018 | 38 | 2018 |
| 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 | 36 | 2019 |
Byron BootsAssociate Professor, University of WashingtonE-mail confirmado em cs.washington.edu