| Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning N Lambert, DS Drew, J Yaconelli, R Calandra, S Levine, KSJ Pister IEEE Robotics and Automation Letters 4 (4), 4224-4230, 2019 | 93 | 2019 |
| Toward controlled flight of the ionocraft: a flying microrobot using electrohydrodynamic thrust with onboard sensing and no moving parts D Drew, N Lambert, C Schindler, K Pister IEEE Robotics and Automation Letters 3 (4), 2807-2813, 2018 | 45 | 2018 |
| On the importance of hyperparameter optimization for model-based reinforcement learning B Zhang, R Rajan, L Pineda, N Lambert, A Biedenkapp, K Chua, F Hutter, ... International Conference on Artificial Intelligence and Statistics, 4015-4023, 2021 | 36 | 2021 |
| Objective Mismatch in Model-based Reinforcement Learning N Lambert, B Amos, O Yadan, R Calandra Learning for Dynamics and Control (L4DC), 2020 | 35 | 2020 |
| Learning generalizable locomotion skills with hierarchical reinforcement learning T Li, N Lambert, R Calandra, F Meier, A Rai IEEE International Conference on Robotics and Automation (ICRA), 413-419, 2020 | 22 | 2020 |
| Enhanced lithium niobate pyroelectric ionizer for chip-scale ion mobility-based gas sensing KB Vinayakumar, V Gund, N Lambert, S Lodha, A Lal IEEE SENSORS, 1-3, 2016 | 10 | 2016 |
| Mbrl-lib: A modular library for model-based reinforcement learning L Pineda, B Amos, A Zhang, NO Lambert, R Calandra arXiv preprint arXiv:2104.10159, 2021 | 8 | 2021 |
| Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning N Lambert, A Wilcox, H Zhang, K Pister, R Calandra IEEE Conference on Decision and Control (CDC), 2880-2887, 2021 | 7 | 2021 |
| AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks MK Andrus, S Dean, TK Gilbert, N Lambert, T Zick IEEE Symposium on Technology and Society (ISTAS), 2021 | 4 | 2021 |
| Nonholonomic yaw control of an underactuated flying robot with model-based reinforcement learning NO Lambert, CB Schindler, DS Drew, KSJ Pister IEEE Robotics and Automation Letters 6 (2), 455-461, 2020 | 4 | 2020 |
| Reward Reports for Reinforcement Learning TK Gilbert, S Dean, N Lambert, T Zick, A Snoswell arXiv preprint arXiv:2204.10817, 2022 | 2 | 2022 |
| Investigating Compounding Prediction Errors in Learned Dynamics Models N Lambert, K Pister, R Calandra arXiv preprint arXiv:2203.09637, 2022 | 1 | 2022 |
| Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems TK Gilbert, S Dean, T Zick, N Lambert Center for Long Term Cybersecurity Whitepaper Series, 2022 | 1 | 2022 |
| Axes for sociotechnical inquiry in AI research S Dean, TK Gilbert, N Lambert, T Zick IEEE Transactions on Technology and Society 2 (2), 62-70, 2021 | 1 | 2021 |
| The Challenges of Exploration for Offline Reinforcement Learning N Lambert, M Wulfmeier, W Whitney, A Byravan, M Bloesch, V Dasagi, ... arXiv preprint arXiv:2201.11861, 2022 | | 2022 |
| Predicting Flying Robot Dynamics with Deep Learning B Li, N Lambert Journal of Student Research 10 (3), 2021 | | 2021 |
| BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control M Selden, J Zhou, F Campos, N Lambert, D Drew, KSJ Pister IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS …, 2021 | | 2021 |
| Learning for Microrobot Exploration: Model-based Locomotion, Sparse-robust Navigation, and Low-power Deep Classification N Lambert, F Toddywala, B Liao, E Zhu, L Lee, K Pister IEEE Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), 2020 | | 2020 |
Roberto CalandraMeta AIПотвърден имейл адрес: fb.com
