| Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards J Mahler, FT Pokorny, B Hou, M Roderick, M Laskey, M Aubry, K Kohlhoff, ... 2016 IEEE international conference on robotics and automation (ICRA), 1957-1964, 2016 | 300 | 2016 |
| Bayesian policy optimization for model uncertainty G Lee, B Hou, A Mandalika, J Lee, S Choudhury, SS Srinivasa arXiv preprint arXiv:1810.01014, 2018 | 29 | 2018 |
| Problems before solutions: Automated problem clarification at scale S Basu, A Wu, B Hou, J DeNero Proceedings of the Second (2015) ACM Conference on Learning@ Scale, 205-213, 2015 | 20 | 2015 |
| Fuzz testing projects in massive courses S Sridhara, B Hou, J Lu, J DeNero Proceedings of the Third (2016) ACM Conference on Learning@ Scale, 361-367, 2016 | 16 | 2016 |
| Privacy-preserving grasp planning in the cloud J Mahler, B Hou, S Niyaz, FT Pokorny, R Chandra, K Goldberg 2016 IEEE International Conference on Automation Science and Engineering …, 2016 | 13 | 2016 |
| Efficient motion planning for problems lacking optimal substructure O Salzman, B Hou, S Srinivasa Twenty-Seventh International Conference on Automated Planning and Scheduling, 2017 | 12 | 2017 |
| Sample-efficient learning of nonprehensile manipulation policies via physics-based informed state distributions L Pinto, A Mandalika, B Hou, S Srinivasa arXiv preprint arXiv:1810.10654, 2018 | 8 | 2018 |
| Posterior sampling for anytime motion planning on graphs with expensive-to-evaluate edges B Hou, S Choudhury, G Lee, A Mandalika, SS Srinivasa 2020 IEEE International Conference on Robotics and Automation (ICRA), 4266-4272, 2020 | 4 | 2020 |
| Guided incremental local densification for accelerated sampling-based motion planning A Mandalika, R Scalise, B Hou, S Choudhury, SS Srinivasa arXiv preprint arXiv:2104.05037, 2021 | 3 | 2021 |
| Stein Variational Probabilistic Roadmaps A Lambert, B Hou, R Scalise, SS Srinivasa, B Boots arXiv preprint arXiv:2111.02972, 2021 | 1 | 2021 |
| Bayesian Residual Policy Optimization:: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts G Lee, B Hou, S Choudhury, SS Srinivasa 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 1 | 2021 |
| Deep conditional generative models for heuristic search on graphs with expensive-to-evaluate edges B Hou, S Srinivasa Online]. Avail-able: https://personalrobotics. cs. washington. edu/workshops …, 0 | 1 | |
| Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes G Lee, S Choudhury, B Hou, SS Srinivasa arXiv preprint arXiv:1810.03048, 2018 | | 2018 |
| Bayesian Residual Policy Optimization G Lee, B Hou, S Choudhury, SS Srinivasa | | |
| Residual Bayesian Q-Learning for Meta-Reinforcement Learning with Experts G Lee, S Choudhury, B Hou, SS Srinivasa | | |
| Collision Posteriors on Graphs with Expensive-to-Evaluate Edges B Hou, S Choudhury, G Lee, M Barnes, SS Srinivasa | | |
